Essays on Strategic Behavior in the U.S. Airline Industry
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree
Doctor of Philosophy in the Graduate School of The Ohio State
University
By
Kerria Measkhan Tan, B.A., M.A.
Graduate Program in Economics
The Ohio State University
2012
Dissertation Committee:
Matthew Lewis, Advisor
James Peck
Huanxing Yang
c Copyright by
Kerria Measkhan Tan
2012
Abstract
In my first dissertation essay, “Incumbent Response to Entry by Low-Cost Carriers in the U.S. Airline Industry,” I analyze the price response of incumbents to entry
by low-cost carriers in the U.S. airline industry. Previous theoretical papers suggest
that airlines might respond to entry by lowering prices to compete harder for existing
customers or they might increase prices to exploit their brand-loyal customers. This
paper tests which effect is more prominent in the airline industry. I find that when one
of four low-cost carriers enters a particular route, legacy carrier incumbents respond
differently than low-cost carrier incumbents to new low-cost carrier entry. Legacy
carriers decrease their mean airfare, 10th percentile airfare, and 90th percentile airfare before and after entry by a low-cost carrier. However, low-cost carriers do not
significantly alter their pricing strategy. The differing incumbent responses can be
attributed to the finding that low-cost carrier entrants tend to match the price set
by rival low-cost carriers in the quarter of entry and tend to enter with a lower price
than that of legacy carrier incumbents. The results also suggest that entry does not
affect price dispersion by incumbent carriers.
Legacy carriers have increasingly outsourced the operation of certain routes to
regional airlines over the past decade. My second dissertation essay, “The Influence
of Low-Cost Carriers on the Use of Regional Airlines,” investigates how low-cost
carriers influence where legacy carriers decide to use regional airlines. I find evidence
ii
that legacy carriers are more inclined to switch to regional airlines on routes where
a low-cost carrier exists. Moreover, legacy carriers tend to not only decrease average
airfares once they start outsourcing but also price match competing low-cost carriers.
However, I do not find evidence that low-cost carriers are effectively deterred from
entering routes where a regional airline is present. The results refute the notion
that regional airlines can serve as an effective barrier to entry, while suggesting that
legacy carriers exploit the more cost-efficient regional airlines in order to lower price
and therefore better compete with low-cost carriers.
My third and final dissertation essay, “The Effect of De-Hubbing on Airfares,”
studies the price effect of de-hubbing, which occurs when an airline ceases hub operations at an airport. Legacy carriers dramatically decrease both the frequency of
flights and the number of seats offered once it de-hubs an airport, whereas their
competitors generally respond by maintaining their capacity level at the airport. As
a result, prices could potentially decrease as the market becomes less concentrated
or increase because the de-hubbing airline’s capacity reduction diminishes the availability of substitutes. I perform an event study using four cases of de-hubbing at
domestic airports between 2001 and 2006 to test which price effect dominates. Not
only do average airfares increase after a legacy carrier de-hubs an airport but also the
de-hubbing airline and its competitors at that airport tend to increase their prices by
a similar percentage. The results suggest that de-hubbing ultimately leads to softer
competition between airlines at the de-hubbed airport.
iii
This is dedicated to my parents, who taught me to work hard and dream big.
iv
Acknowledgments
It seems like only yesterday that Steven Chen, Jason Yau, and I would meet up in
the study rooms at Geisel Library on the University of California, San Diego campus
in order to study for the math classes that we took together. I remember a particular
instance in which I was working through a linear algebra problem using dry erase
markers that we “borrowed” from classrooms on campus while taking advantage of
the study rooms’ one-way windows. They both stopped me and told me that I have
a particular knack for teaching. It was at this moment that I realized that I truly
enjoyed these study sessions and that teaching was something that I wanted to pursue
for a living. Fast forward about ten years and now I am on the verge of obtaining
my Ph.D. in Economics and starting my new job as an Assistant Professor at Loyola
University Maryland. Surely, there have been others along the way that have helped
me achieve my academic goals. This serves as an inadequate, yet sincere thank you
to the people who have particularly influenced my life.
One of the biggest reasons why I came to Ohio State was the opportunity to work
with my advisor, Matt Lewis. I remember meeting him during my recruitment trip
and discussing my senior honors thesis on the Southwest Effect. I cannot imagine
having a better advisor than Matt. He gives me the freedom and encouragement to
pursue the topics that I am interested in, but is not afraid to let me know if he thinks
I am not being as productive as I should be. I feel like I can brainstorm openly in
v
front of him without the fear that he will belittle me if I am incorrect or start to stray
towards the wrong path. He is always available when I need to talk and has given
me such great advice on research, presentations, and other things. I have the utmost
respect for him and will always be greatly indebted to him for the help he has given
me over these past six years.
Jim Peck and Huanxing Yang serve as the other two members of my dissertation
committee. I learned greatly from them when I took their second year microeconomic
theory courses and wanted their help to ensure that the empirical results in my
research are rooted in theoretical foundations. Jim, in particular, helped boost my
confidence when I was about to go on fly-outs while on the job market. I will miss
discussing the English Premier League with Huanxing, especially when it comes to
the rivalry between our two beloved teams, Liverpool and Manchester United.
Other faculty members at Ohio State have also been very influential. Belton
Fleisher became a close confidant and someone that I could always turn to for advice. I
always knew that Hajime Miyazaki had my best interests in mind whenever he gave me
his opinions regardless of whether they were solicited or not. Bruce Weinberg always
believed in my teaching potential and nominated me for several teaching awards. He
also served as a teaching reference when I was on the job market. I got to know Don
Haurin well when we formed arguably the best battery in the history of the Ohio
State summer softball league. Similarly, Bill Dupor and I built up a rapport during
Saturday morning pick-up basketball games and was gracious enough to serve as the
fourth member of my candidacy committee. Finally, Lucia Dunn vastly improved my
interviewing skills, especially when it came to the five minute talk on my job market
paper. I also have a much firmer hand shake grip thanks to her.
vi
I also would not have been able to get through my graduate studies without a
great core of friends. I developed a close friendship with Michael Sinkey after all the
time we spent watching and playing sports, as well as working at various coffee shops
in and around the Columbus area. Jeff Baird and Neil Dalvi ensured that I struck
the right balance between work and play. Saif Mekhari was never shy to give me
his advice on how to best excel in the program based on his experiences. I would
not have been as confident and prepared for job market interviews had I not spent
endless hours practicing with Matt Jones and Brandon Restrepo. Matt Dicker and
Dan Gallardo were always a phone call or Google Chat message away whenever I
needed to talk to the friends that knew me best. Last but surely not least, Cassandra
Lissey has become my best friend and someone I will love and trust for the rest of
my life.
As wonderful as the aforementioned people have been, they cannot compare to
the impact that my family has had in my life. Sophia is the best little sister that I
could ever ask for. My grandparents on both sides of my family have always spoiled
me with love and attention. However, my parents have had the biggest influence in
my life. I would not have been as fascinated with the airline industry if it were not
for my dad, who worked for Korean Air for well over twenty years. He gave me the
confidence to chase after my dreams and to learn from my mistakes along the way.
He has taught me to always “turn poison into medicine.” My mom is the most selfless
person I know and is such a great inspiration to me. She has taught me to work hard
and to never give up on my goals. I surely would not have been able to accomplish
any of this without her love and support. In the end, I just hope that I have made
my family proud.
vii
Vita
September 30, 1984 . . . . . . . . . . . . . . . . . . . . . . . . . Born - Northridge, California.
2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.A. Economics,
University of California, San Diego.
2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M.A. Economics,
The Ohio State University.
2006-2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graduate Teaching Associate,
The Ohio State University.
Fields of Study
Major Field: Economics
viii
Table of Contents
Page
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ii
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
iv
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
v
Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
viii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xiii
1.
Incumbent Response to Entry by Low-Cost Carriers in the U.S. Airline
Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1
1.2
1.3
Industry Background and Potential Effect of Entry . . . . . . . . .
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1 Estimation Strategy . . . . . . . . . . . . . . . . . . . . . .
1.3.2 Incumbent Price Response to Entry: Mean Airfare . . . . .
1.3.3 Incumbent Price Response to Entry: 10th Percentile Airfare,
90th Percentile Airfare, and Gini Coefficient . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
30
The Influence of Low-Cost Carriers on the Use of Regional Airlines . . .
32
2.1
2.2
2.3
2.4
2.5
34
37
39
52
63
1.4
2.
1
Industry Structure . . . . . . . . . . . . . . . .
Data . . . . . . . . . . . . . . . . . . . . . . . .
Motivations for Regional Airline Entry . . . . .
The Effect of Regional Airline Entry on Pricing
Conclusion . . . . . . . . . . . . . . . . . . . .
ix
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3.
The Effect of De-Hubbing on Airfares . . . . . . . . . . . . . . . . . . . .
66
3.1
3.2
3.3
69
74
79
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendices
A.
B.
C.
81
Tables for “Incumbent Response to Entry by Low-Cost Carriers in the
U.S. Airline Industry” . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
Data Construction for “The Influence of Low-Cost Carriers on the Use of
Regional Airlines” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
85
Robustness Checks for “The Influence of Low-Cost Carriers on the Use of
Regional Airlines” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
94
C.1 Pooled Logit Regression Results . . . . . . . . . . . . . . . . . . . .
C.2 Price Matching Windows . . . . . . . . . . . . . . . . . . . . . . .
94
95
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
x
List of Tables
Table
Page
1.1
Frequency of Price Matching by Entrant . . . . . . . . . . . . . . . .
12
2.1
Entry by Regional Airlines . . . . . . . . . . . . . . . . . . . . . . . .
42
2.2
Entry by Low-Cost Carriers (Main Results) . . . . . . . . . . . . . .
45
2.3
Entry by Southwest Airlines vs. Other Low-Cost Carriers . . . . . . .
46
2.4
Summary Statistics (Truncated Sample) . . . . . . . . . . . . . . . .
49
2.5
Entry by Low-Cost Carriers (Selected Sample) . . . . . . . . . . . . .
51
2.6
Price Response to Outsourcing . . . . . . . . . . . . . . . . . . . . . .
56
2.7
Frequency of Price Matching Before Outsourcing . . . . . . . . . . . .
58
2.8
Frequency of Price Matching After Outsourcing . . . . . . . . . . . .
59
2.9
Price Matching by Legacy Carriers . . . . . . . . . . . . . . . . . . .
62
3.1
Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . .
70
3.2
Capacity Before and After De-Hubbing . . . . . . . . . . . . . . . . .
73
3.3
Difference-in-Differences Results . . . . . . . . . . . . . . . . . . . . .
76
3.4
Difference-in-Difference-in-Differences Results . . . . . . . . . . . . .
78
A.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
xi
A.2 Incumbent Price Response to Actual Entry . . . . . . . . . . . . . . .
82
A.3 Legacy Carrier Incumbent Price Response to Actual Entry . . . . . .
83
A.4 Low-Cost Carrier Incumbent Price Response to Actual Entry . . . . .
84
B.1 Regional Airline Partnerships . . . . . . . . . . . . . . . . . . . . . .
91
B.2 Summary Statistics (Entry Sample) . . . . . . . . . . . . . . . . . . .
92
B.3 Summary Statistics (Legacy Carrier Subsample) . . . . . . . . . . . .
93
B.4 Summary Statistics (Low-Cost Carrier Subsample) . . . . . . . . . .
93
C.1 Entry by Regional Airlines (Pooled Logit Model) . . . . . . . . . . .
96
C.2 Entry by Regional Airlines (Pooled Logit Model with Time Dummies)
97
C.3 Entry by Low-Cost Carriers Airlines (Pooled Logit Model) . . . . . .
98
C.4 Entry by Low-Cost Carriers (Pooled Logit Model with Time Dummies) 99
C.5 Frequency of Price Matching Before Outsourcing: 15% Window . . . 100
C.6 Frequency of Price Matching After Outsourcing: 15% Window . . . . 101
C.7 Frequency of Price Matching Before Outsourcing: 25% Window . . . 102
C.8 Frequency of Price Matching After Outsourcing: 25% Window . . . . 103
xii
List of Figures
Figure
Page
1.1
Incumbent Response to Entry: Mean Airfare . . . . . . . . . . . . . .
18
1.2
Legacy Carrier Incumbent Response to Entry: Mean Airfare . . . . .
19
1.3
Low-Cost Carrier Incumbent Response to Entry: Mean Airfare . . . .
20
1.4
Legacy Carrier Incumbent Response to Entry: 10th Percentile Airfare
23
1.5
Low-Cost Carrier Incumbent Response to Entry: 10th Percentile Airfare 24
1.6
Legacy Carrier Incumbent Response to Entry: 90th Percentile Airfare
1.7
Low-Cost Carrier Incumbent Response to Entry: 90th Percentile Airfare 26
1.8
Legacy Carrier Incumbent Response to Entry: Gini Coefficient . . . .
27
1.9
Low-Cost Carrier Incumbent Response to Entry: Gini Coefficient . .
27
2.1
Number of Passengers Flown by Operating Carrier . . . . . . . . . .
36
3.1
Number of Flights by De-Hubbing Airline . . . . . . . . . . . . . . .
71
xiii
25
Chapter 1: Incumbent Response to Entry by Low-Cost
Carriers in the U.S. Airline Industry
When a firm enters a market consisting of a brand-loyal segment and a pricesensitive segment, there are two effects on the incumbents’ pricing strategy: the
competitive effect and the displacement effect. Once the entrant enters, the incumbent
would continue to decrease prices in order to keep customers because the incumbent
firm’s individual demand curve decreases and becomes more elastic due to an increase
in the number of substitutes. Klemperer [24] and Perloff and Salop [28] refer to this as
the competitive effect. On the other hand, Rosenthal [31] and Hollander [20] provide
the theoretical foundation for the displacement effect, in which entry can actually
cause incumbents to increase their prices due to the existence of the two market
segments. If entrants are known to cater toward price-sensitive consumers, then
incumbents may be best served by increasing prices. In effect, these incumbents will
focus their attention on their brand-loyal consumers, who will continue purchasing
from them even if an entrant offers lower prices. This strategy will maximize profits
whenever the increase in price dominates the effect of the quantity decrease. Since
both effects can occur simultaneously, the net effect on prices depends on which effect
is more prominent.
1
The growth of several low-cost carriers over the past decade allows for the ability
to study whether the competitive effect or the displacement effect is more dominant
in the airline industry. This paper focuses on two types of airlines: legacy carriers and
low-cost carriers. Legacy carriers are airlines that operate a hub-and-spoke network1
and were founded prior to the industry’s deregulation in 1978, while low-cost carriers
typically implement a point-to-point network2 and emerged after deregulation. The
purpose of this paper is to study the price response of both legacy carrier and low-cost
carrier incumbents when a low-cost carrier enters a new route.
The key result of the paper is that legacy carrier incumbents react differently
than low-cost carrier incumbents to entry by low-cost carriers. First, legacy carrier
incumbents significantly decrease average one-way airfares the quarter before and the
quarter after actual entry by a low-cost carrier. Moreover, low-cost carrier incumbents
do not seem to significantly respond to entry by a rival low-cost carrier. Second, I
study how the incumbents’ distribution of prices changes due to entry by a low-cost
carrier. The 10th percentile prices decrease by about the same amount as the 90th
percentile prices so that no significant change occurs to the overall price distribution
of the airfares. As such, there is no statistically significant change to price dispersion. Prices decrease all along the distribution of prices almost equally so that price
dispersion does not change. Finally, low-cost carrier entrants are likely to enter with
an average price that is around the average price of low-cost carrier incumbents and
less than that of legacy carrier incumbents. Hence, one reason why low-cost carrier
incumbents do not significantly respond to entry by a rival low-cost carrier is because
1
A hub-and-spoke network concentrates passengers from several satellite airports (spokes) at a
major airport (hub) en route to their final destination airport.
2
A point-to-point network provides more direct service with fewer connections than a hub-andspoke network.
2
the entrant tends to match the price of the low-cost carrier incumbent. Meanwhile,
there is downward pressure on legacy carrier incumbents’ prices since the entrant sets
a price that is likely to be lower than their price. Although both the story based
on the competitive effect and the displacement effect seem to be plausible in the airline industry, the results support the claim that the competitive effect dominates the
displacement effect.
The empirical analysis regarding incumbent response to entry resembles that in
Goolsbee and Syverson [16], which examines the effect of potential competition by
Southwest Airlines on rivals’ pricing strategies. They find that carriers decrease their
prices when they face potential competition with Southwest Airlines, suggesting that
incumbents decrease their prices when entry is merely threatened. They estimate a
two-way fixed effects model, incorporating time dummies to estimate the effects of
potential competition on prices. In effect, they conduct an event study by examining
the incumbents’ prices before, during, and after Southwest Airlines enters both airports of a route. I expand upon their work by modifying their estimation strategy so
that I can examine the effect of actual competition3 when entry actually occurs by
not only Southwest Airlines but also other low-cost carriers.
Gerardi and Shapiro [13] investigate how an airline’s ability to price discriminate
on a given route is affected by competition. They find that price dispersion decreases
3
It is important to note the differences between the different types of competition in the airline
industry. Suppose that Southwest Airlines operates at the San Diego International Airport (SAN)
and the San Francisco International Airport (SFO). Suppose further that Southwest Airlines services
the SAN-SFO route. Actual competition exists when two airlines service the same route at the same
time. United Airlines is said to actually compete with Southwest Airlines if United also services the
SAN-SFO route at the same time as Southwest Airlines. Now suppose that Southwest Airlines also
operates at the Los Angeles International Airport (LAX), but does not service the SAN-LAX route.
Potential competition exists when a firm operates at two airports but does not service the route
linking both airports that is served by another airline. United Airlines potentially competes with
Southwest Airlines if United services the SAN-LAX route at the same time that Southwest Airlines
operates at both airports but does not service the SAN-LAX route.
3
with competition, in stark contrast to Borenstein and Rose [4]. Both my paper
and these previous papers studies how a firm responds to competition. However,
the previous literature is interested in estimating the effect of competition on price
dispersion in the airline industry as a whole, whereas this paper examines how price
dispersion changes upon entry by a low-cost carrier. Naturally, endogeneity problems
arise with these types of studies. I try to minimize the endogeneity problem by
looking at entry as opposed to a smooth measure of competition, such as the routelevel Herfindahl-Hirschman Index. Moreover, the previous literature assumes that the
effect of competition is the same for all airlines, while I allow the effect of entry on price
dispersion to vary across different airlines. I am interested in how the incumbents
respond to entry by each low-cost carrier.
One of the key results of this paper is that an increase in competition does not
lead to a significant change in the incumbent’s price dispersion, which differs from
the findings from both Gerardi and Shapiro [13] and Borenstein and Rose [4]. This
can be attributed to the differing identification strategy in this paper from the two
previous studies, which regress measures for price dispersion on several control variables, including various proxies for competition. Their key findings stem from the
sign and strength of the estimated coefficient for the competition variables. The major difference in the analysis of this study to the previous literature is that this paper
uses entry as opposed to the route-level Herfindahl-Hirschman Index to identify competitor’s response to competition. I analyze the pricing behavior right around entry
by performing an event study that captures the immediate effect of competition on
price dispersion. By investigating how the Gini coefficient and the tails of the price
4
distribution change around the entry period, this paper is able to shed new light on
the effect of competition on the price distribution of rival firms.
1.1
Industry Background and Potential Effect of Entry
The competitive structure of the U.S. airline industry has gone through several
changes since deregulation in 1978. Airlines have since experienced more flexibility
in their route network and pricing strategies. It is easier to enter routes that were
once heavily regulated by the Civil Aeronautics Board. As a result, there has been
an influx of entry in the past two decades by low-cost carriers. These airlines include
AirTran Airways, JetBlue Airways, Southwest Airlines, and Spirit Airlines. Low-cost
carriers are able to charge low prices due to their efficient cost structure, benefitting
from the implementation of a point-to-point network, usage of non-unionized labor,
and operation of the same type of aircraft.4 This is in stark contrast to legacy
carriers, which implement a hub-and-spoke network, use mostly unionized labor, and
operate with a variety of different aircrafts. Legacy carriers, which include American
Airlines, Continental Airlines, Delta Air Lines, Northwest Airlines, United Airlines,
and US Airways, get their name because they were founded and operated prior to
deregulation.
Low-cost carriers have gained market share in the airline industry, particularly in
the past decade. In 1997, low-cost carriers flew over 37 million passengers total and
accounted for 21.4% of the market share of all passengers flying domestically. In 2007,
the number of passengers flying with low-cost carriers increased to over 75 million
passengers, resulting in a 36.2% market share of all domestic travel. This growth can
4
For example, Southwest Airlines exclusively uses Boeing 737 jets.
5
be partly attributed to the expansion of the low-cost carriers’ route network. Among
the top 1000 most traveled routes, there were 494 instances of entry from 1993:Q1
to 2007:Q4 by low-cost carriers, with AirTran Airways entering 224 routes, JetBlue
Airways entering 68 routes, Southwest Airlines entering 150 routes, and Spirit Airlines
entering 52 routes. Each route consists of a particular one-way airport-pair. For
example, two routes were considered to be entered when Southwest Airlines started
flying from Orlando International Airport to Philadelphia International Airport and
vice versa in 2004:Q2. This paper examines four currently operating low-cost carriers
(AirTran Airways, JetBlue Airways, Southwest Airlines, and Spirit Airlines), who
have grown substantially over the past two decades and who remain significant players
in the airline industry today.
Previous research has studied the effect of brand loyalty on the demand for flying.
Borenstein [2] and Gilbert [14] describe how airlines employ marketing schemes in the
form of frequent flier programs in order to create and strengthen consumers’ brand
loyalty for that particular airline. Consumers enroll in an airline’s frequent flier program and accumulate credit each time they fly with that particular airline. Members
can redeem their credit for free flights, upgrades, or other rewards from that airline.
Brand-loyal consumer effectively experience a switching cost upon enrollment in a
particular carrier’s frequent flier program. Kim, Shi, and Srinivasan [23] explore how
these marketing programs can create two market segments: brand-loyal consumers
and price-sensitive consumers.5 Brand-loyal consumers tend to be members of a
particular airline’s frequent flier program and become disposed to purchasing more
flights on that airline. Price-sensitive consumers simply look to fly with the airline
5
Kim, Shi, and Srinivasan [23] refer to the brand-loyal consumers and price-sensitive consumers
as the heavy-user segment and the light-user segment, respectively.
6
charging the lowest price for a given route. Borenstein [2] explains how consumers
are inclined to participate in a particular airline’s frequent flier program when they
live in that airline’s hub city. For example, Delta Air Lines uses Hartsfield-Jackson
Atlanta International Airport as a hub. Consumers in Atlanta are more likely to not
only fly with Delta but also enroll in Delta’s frequent flier program in order to benefit
from the wide selection of markets serviced out of Atlanta. This ultimately serves to
hook passengers to that particular airline, who can exploit their brand-loyal segment
by increasing prices without the fear of losing a significant amount of their market
base. In other words, members of an airline’s frequent flier program will continue
to purchase from that carrier even if they were charged a higher price because these
consumers want to obtain an award after purchasing a certain amount of trips from
that airline. Therefore, brand loyalty serves as a switching cost for consumers.
There is empirical evidence for the displacement effect in industries which parallel
the airline industry. Using data on the pharmaceutical industry, Grabowski and Vernon [17] found that entry by generic drugs induced firms selling branded prescription
drugs to target consumers with inelastic demand, leaving generic drugs to focus on
consumers with more elastic demand. This led to an increase in the price of branded
drugs, exemplifying the case when the displacement effect is more prominent than
the competitive effect. The airline industry can be considered analogous to the prescription drug market in the sense that brand loyalty is prevalent in both industries
with incumbent carriers similar to branded prescription drugs and low-cost carrier
entrants akin to generic drugs.
I ask whether incumbent airlines segmented the market in a similar fashion once
a low-cost carrier entered a route. The displacement effect dominates if incumbent
7
airlines focus solely on brand-loyal consumers, resulting in an increase of the incumbent’s average price. Incumbents can focus on the brand-loyal segment of the
market and allow entrants to service the price sensitive market segment, which would
increase price dispersion. However, the competitive effect dominates if entry by lowcost carriers leads to stronger competition for price sensitive consumers, resulting in
a decrease in the incumbent’s average price. Furthermore, the decrease in price at
the low end of the price distribution could induce incumbents to also decrease prices
at the high end of the distribution in order to prevent brand-loyal consumers from
becoming more price sensitive. If there was a substantial difference between full fares
and discount fares, then brand-loyal consumers would substitute between competing
carriers. This paper sets out to investigate whether competition for price sensitive
consumers induces price competition for brand-loyal consumers as well.
The competitive effect also seems to be a credible story behind how incumbents
respond to entry by low-cost carriers. Morrison [27] and Vowles [33] both document
evidence that incumbents decrease price when Southwest Airlines enters a new market
– the so-called Southwest Effect. This supports the claim that the competitive effect
could dominate the displacement effect. However, given the nature of the airline industry, it is plausible that the displacement effect dominates as in the pharmaceutical
industry. Therefore, it could be argued that incumbents would increase their price in
response to entry by a low-cost carrier. This paper serves to empirically test whether
the competitive effect story or the displacement effect story characterizes the entry
effect of low-cost carriers in the U.S. airline industry.
8
1.2
Data
The data used for this paper was collected from the Airline Origin and Destination Survey (DB1B), which is published quarterly by the Bureau of Transportation
Statistics. It is a ten percent sample of airline tickets from carriers flying domestic
routes. From this database, I collect information on the origin, destination, non-stop
distance between endpoints, ticketing carrier, market fare,6 and number of passengers paying a particular market fare. The market fare is the one-way price paid by a
passenger for a specific origin-destination route on a particular carrier.
I eliminate all observations where the market fare is less than $10 or the distance
was equal to zero. Observations with an unidentified ticketing carrier were dropped.
Only observations related to nonstop flights were kept. Observations pertaining to
carriers who have less than 1% of the traffic on a given route were eliminated. Finally,
the sample was restricted to the 1000 routes with the highest number of passengers
from 1993:Q1 to 2007:Q4. The dataset contains information on 2.67 trillion passengers
over the 15 year time period, which corresponds to roughly 45 million passengers per
quarter.7
In order to be identified as an instance of actual entry, the entrant must have not
operated on the route for twelve quarters prior to the quarter of entry and remain
on the route for two quarters after entry. The entrant must also service at least 100
passengers in the quarter of entry. Two robustness checks on the identification of
entry were performed. There are some cases in which two or more low-cost carriers
6
Market fare is calculated by the Bureau of Transportation Statistics as the itinerary yield multiplied by the number of miles flown.
7
This paper focuses on the effect of six legacy carriers and four low-cost carriers. The total
number of passengers serviced by these ten carriers represents 81.9% of the sample.
9
entered a particular route within the sample period. One concern would be that
the incumbents would respond to the first entrant, but not necessarily to the second
entrant. The first robustness check isolates the first-entrant response by identifying
entry only if there was no low-cost carrier servicing the route prior to entry. Another
concern may arise if incumbents attempt a predatory pricing scheme in order to deter
entry.8 Since my identification rule is that the entrant must remain on the route
for only two subsequent quarters after entry, the price response would capture the
initial price decrease and subsequent price increase consistent with a predatory pricing
scheme. The second robustness check rules out predatory pricing effects by requiring
that the entrant must continue to operate on the route for at least eight quarters after
entry. The results for each robustness check remain qualitatively consistent with the
main results of this paper.
There are three carrier classifications in the DB1B: reporting carrier, operating
carrier, and ticketing carrier. Reporting carrier refers to the carrier who submits the
information to the Bureau of Transportation Statistics. Operating carrier refers to
the carrier who conducted the actual service of air transportation. Ticketing carrier
refers to the carrier who issued the passenger the ticket for the flight. In most cases,
the three are the same. However, there are cases in which the three are different. For
instance, a regional airline could operate the flight under a codeshare agreement with
the ticketing carrier. The scope of this paper focuses on how the entry by a low-cost
carrier affects the incumbents’ prices. The brand name competition is based at the
ticketing-level rather than at the operating-level. Moreover, the consumer’s decision
on a reservation is based on the ticketing carrier. In other words, consumers often
8
See Elzinga and Mills [8] for details on the Spirit Airlines v. Northwest Airlines predatory
pricing case.
10
ignore who the operating carrier is or the fact that the flight is a codeshare flight
with another carrier. At the time that the reservation is made, passengers base their
purchase on who they purchase the ticket from. For these reasons, I use the ticketing
carrier classification here.
1.3
Empirical Analysis
In order to take a preliminary look at incumbent response to entry by low-cost
carriers, I analyze the average prices set by incumbents and the entrant in the quarter
of actual entry. I report the frequency and percentage that an entrant enters with
an average price higher than, equal to, or lower than that set by the incumbents.
In order to do this, I create a price window of $20 around the average price set by
each incumbent.9 Price matching occurs if the entrant’s average price is within the
incumbent’s $20 price window in the quarter of entry. In order for the entrant to
have been determined to set a price higher (lower) than the incumbent’s price, the
entrant’s average price must be at least $20 greater than (less than) the incumbent’s
price. In order to check the robustness of the results, price windows of $10, $15,
$25, and $30 were calculated. The results are qualitatively similar. The quantitative
differences between price windows stem from the fact that the percentage of price
matching increases as the size of the window increases. Table 1.1 summarizes the
results using a $20 price window.
Low-cost carrier entrants tend to set an average price that is lower than the legacy
carrier incumbents’ average price in the quarter of entry. For example, Southwest
Airlines enters at an average price that is lower than American Airlines’s price on
9
The average price in the sample is $170.35 so the $20 price window accounts for roughly a 10%
cushion in prices.
11
Entrant
Entrant
Entrant
12
Entrant’s price > incumbent’s price
Incumbent
American Continental
Delta
Northwest
United
US Airways
AirTran
10 (8.1%)
8 (8.1%)
4 (2.0%)
8 (8.2%)
7 (6.4%)
10 (6.4%)
JetBlue
1 (2.3%)
3 (9.4%)
5 (8.1%)
4 (22.2%) 4 (11.1%)
3 (6.8%)
Southwest 6 (7.9%)
4 (5.2%)
5(3.9%)
4 (5.7%)
2 (2.2%)
6 (5.0%)
Spirit
0 (0.0%)
0 (0.0%)
1 (2.2%)
0 (0.0%)
0 (0.0%)
1 (2.5%)
Entrant’s price = incumbent’s price
Incumbent
American Continental
Delta
Northwest
United
US Airways
AirTran
36 (29.3%) 34 (34.3%) 60 (29.3%) 45 (46.4%) 28 (25.5%) 53 (33.8%)
JetBlue
20 (46.5%) 14 (43.8%) 24 (38.7%) 4 (22.2%) 8 (22.2%) 12 (27.3%)
Southwest 26 (34.2%) 25 (32.5%) 48 (37.5%) 35 (50.0%) 23 (25.8%) 49 (40.8%)
Spirit
5 (16.7%)
6 (24.0%)
11 (23.9%)
1 (4.0%)
1 (3.3%)
8 (20%)
Entrant’s price < incumbent’s price
Incumbent
American Continental
Delta
Northwest
United
US Airways
AirTran
77 (62.6%) 57 (57.6%) 141 (68.8%) 44 (45.4%) 75 (68.2%) 94 (59.9%)
JetBlue
22 (51.2%) 15 (46.9%) 33 (53.2%) 10 (55.6%) 24 (66.7%) 29 (65.9%)
Southwest 44 (57.9%) 48 (62.3%) 75 (58.6%) 31 (44.3%) 64 (71.9%) 65 (54.2%)
Spirit
25 (83.3%) 19 (76.0%) 34 (73.9%) 24 (96.0%) 29 (96.7%) 31 (77.5%)
JetBlue
Southwest
–
5 (12.5%)
n/a
0 (0.0%)
2 (50.0%)
n/a
–
10 (62.5%)
AirTran
n/a
6 (35.3%)
14 (40.0%)
4 (66.7%)
Southwest
10 (25.0%)
2 (100.0%)
n/a
1 (6.3%)
JetBlue
Southwest
–
25 (62.5%)
n/a
0 (0.0%)
2 (50.0%)
n/a
–
5 (31.3%)
JetBlue
–
n/a
0 (0.0%)
–
AirTran
n/a
9 (52.9%)
19 (54.3%)
2 (33.3%)
AirTran
n/a
2 (11.8%)
2 (5.7%)
0 (0.0%)
Table 1.1: Frequency of Price Matching by Entrant
Spirit
2 (25.0%)
0 (0.0%)
0 (0.0%)
n/a
Spirit
6 (75.0%)
4 (100.0%)
4 (50.0%)
n/a
Spirit
0 (0.0%)
0 (0.0%)
4 (50.0%)
n/a
44 of 76 (57.9%) instances of entry. In other words, Southwest Airlines is likely to
undercut American Airlines’s average price in the quarter that they enter that route,
conditional on the fact that American Airlines is an incumbent carrier. It is very
rare for a low-cost carrier to set a price that is higher than that of a legacy carrier
incumbent. In fact, Southwest Airlines sets a price that is at least $20 higher than
the average price set by United Airlines on only 7 of 110 (6.4%) of the routes that
Southwest Airlines entered and United Airlines is an incumbent. The results suggest
that legacy carrier incumbents may face downward pressure on their prices since they
are being undercut by low-cost carrier entrants.
Low-cost carrier entrants tend to price match the average price set by low-cost
carrier incumbents. On 19 of 35 (54.3%) of the routes in which Southwest Airlines
enters and AirTran Airways is an incumbent, Southwest Airlines ends up setting an
average price that is within $20 of AirTran’s price. The results for the other lowcost entrants suggests that they are more likely to price match rival low-cost carriers
than incumbent legacy carriers, whose prices tend to be more expensive than low-cost
carriers. Therefore, low-cost carrier incumbents might not need to change their prices
since there is weak price competition from the entering low-cost carrier. The differing
responses by legacy carrier incumbents and low-cost carrier incumbents foreshadow
the results presented in this section of the paper.
I study three different responses to entry in order to give a more complete analysis
on the entry effect of low-cost carriers. First, I examine how incumbents change
their mean airfare before and after actual entry by a low-cost carrier. Second, I
investigate how the incumbents’ price distribution of airfares is affected by that entry.
In particular, I look at how the tails of the distribution (10th percentile airfare and
13
90th percentile airfare) change before and after entry. I also examine the entry effect
on the incumbent’s Gini coefficient, which serves as a proxy for price dispersion. The
Gini coefficient is commonly used10 as the measure for fare inequality to reflect the
fact that different passengers end up paying different prices for the same flight serviced
by a particular carrier. The Gini coefficient is constructed to be between zero and
one, where inequality increases as the Gini coefficient increases. In other words, a
Gini coefficient of zero represents perfect equality, whereas a Gini coefficient of one
signifies perfect inequality. In the context of the airline industry, a Gini coefficient
of zero means that everyone pays the same price for a specific route serviced by a
particular carrier, whereas an increase in the carrier’s Gini coefficient shows that there
is more price dispersion on a particular route. Finally, I investigate whether low-cost
carrier entrants set their price below, at, or above the incumbents’ prices when they
enter a new route.
1.3.1
Estimation Strategy
Following the estimation strategy in Goolsbee and Syverson [16], I use a twoway fixed effects model to identify the entry effects on incumbents’ prices. Four
dependent variables were used, including the logged mean airfare (lnprice), the logged
10th percentile price (lnp10), the logged 90th percentile price (lnp90), and the logodds ratio of the Gini coefficient (loddGini).11 Following Gerardi and Shapiro [13],
the log-odds ratio of the Gini coefficient is used to account for the fact that the
Gini coefficient is bounded between zero and one. I control for the carrier’s market
10
Borenstein and Rose [4], Hayes and Ross [18], and Gerardi and Shapiro [13] all use the Gini
coefficient in their estimation strategy.
i
h
G
11
.
The log-odds ratio of the Gini coefficient (G) is defined as loddGini = ln (1−G)
14
share on the route, the arithmetic mean of the market share for a carrier at the two
endpoints, the Herfindahl Index of the route, the arithmetic mean of the Herfindahl
Index at the two endpoints, and the geometric mean of metropolitan statistical area
(MSA) population of the two endpoints. The market share variables are both based
on the number of passengers. MSA population data were obtained from Local Area
BEARFACTS published by the Bureau of Economic Analysis. I also include carrierroute fixed effects and carrier-year-quarter fixed effects. I cluster the standard errors
by route-carrier to account for correlation between a route-carrier combination over
time. Table A.1 in Appendix A provides summary statistics.
The basic specification is as follows:
yijt = γij + µt +
12
X
βτ entryj,t0 +τ + Xijt α + ijt ,
(1.1)
τ =−12
where yijt is either lnpriceijt , lnp10ijt , lnp90ijt , or loddGiniijt for carrier i on route
j in time t, γij is the carrier-route fixed effects, µt is the year-quarter fixed effects,
entryj,t0 +τ are the time dummies that specify the lag/forward of the low-cost carrier
actually entering a route, and Xijt are the control variables explained above.
The two-way fixed effects model contains 25 time dummies that account for 12
quarters before actual entry to 12 quarters after actual entry, including the actual
quarter of entry.12 The estimates of the time lags/forwards of entry show the relative
sizes of logged one-way average airfare in the dummy period versus its average value
in the excluded period (the thirteenth to sixteenth quarters before entry). Table A.2
in Appendix A summarizes the results of the time dummies for each low-cost carrier
entrant in the case where all incumbent carriers (legacy carriers, low-cost carriers,
12
It is important to maintain “clean” windows so particular care was exhibited to ensure that no
other carrier entered that route within the 25 quarter window. This reduced the number of entered
markets in the sample, but would ensure consistent and accurate regression estimates.
15
and other carriers13 ) are accounted for. Column 3 depicts the results of all incumbent
carriers to entry by Southwest Airlines. Since the dummies are mutually exclusive,
an incumbent sets a price that is 12.24% lower,14 on average, in the time period
immediately after actual entry (t0 + 1) relative to the excluded period (the thirteenth
to sixteenth quarters before entry). In other words, the estimates are not additive.
In order to track the price changes by incumbents in response to entry by a
particular carrier, I create price paths based on the coefficients of the time dummies
in the two-way fixed effects model. The price data is based only on incumbents’
prices so we can interpret the results as the incumbents’ pricing response to entry by
a particular carrier. I transform the estimates in order to interpret the coefficients as
relative percent change in price.15 The term “relative” can be interpreted as being
relative to prices in the excluded time period. Entry occurs at time period 0 with
negative time values signifying the quarter before actual entry and positive time values
signifying the quarter after actual entry. The solid line is the transformation of the
point estimates from the model with the dotted lines representing the 95% confidence
interval. If prices are constant throughout (no change in prices by incumbents), then
this can be considered as the incumbents not responding to entry by any sort of price
changes. If prices are less than zero and statistically significant before actual entry,
then this provides evidence for preemptive price cutting.
13
Not all carriers are characterized as either a legacy carrier or a low-cost carrier. For example,
ATA Airlines is a charter airline yet was an incumbent when Southwest Airlines entered the Los
Angeles International Airport to Philadelphia International Airport route in 2004:Q2.
14
The percent change relative to the excluded period is found by exp(-0.1306) - 1 = -0.1224.
15
The point on the figure associated with the relative price change by all incumbents a quarter
after Southwest Airlines enters would be -0.1224, instead of the actual regression estimate of -0.1306.
16
1.3.2
Incumbent Price Response to Entry: Mean Airfare
Incumbent airlines can potentially respond to entry by low-cost carriers in either
one of two ways. The incumbent could decrease their prices before entry occurs in
order to enforce the brand loyalty of their consumers, while enhancing their attractiveness to price-sensitive consumers. Prices could continue to drop even after entry
occurs as the incumbent responds to the reduction in their respective demand due to
an influx of substitutes. Conversely, entry could induce incumbents to actually increase prices so that they could exploit the switching costs inherent in the brand-loyal
market segment. This might occur if the effect of an increase in prices can more than
offset the effect of a decrease in quantity so that profits ultimately increase. I check
to see which of these stories holds true in the airline industry by examining how the
incumbents’ mean airfare changes before and after actual entry by a low-cost carrier.
Figure 1.1 illustrates the price paths for all incumbents (legacy carrier, low-cost
carriers, and other carriers) in response to entry by either AirTran Airways (Figure
1(a)), JetBlue Airways (Figure 1(b)), Southwest Airlines (Figure 1(c)), and Spirit
Airlines (Figure 1(d)). These price paths essentially graph out the time dummies
from the regression results summarized in Table A.2 in Appendix A. Again, these
estimates can be interpreted as the percentage price change relative to the excluded
period (the thirteenth to sixteenth period before entry). Morrison (2001) and Vowles
(2001) both examine price changes the quarter before and the quarter after actual
entry by Southwest Airlines. They find that incumbents significantly decrease their
prices before and after entry by Southwest Airlines. Thus, I focus my analysis on the
price response in the quarter before to the quarter after actual entry occurs. However,
17
I broaden their analysis to examine the type of price effect induced by entry by other
low-cost carriers.
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.1: Incumbent Response to Entry: Mean Airfare
Each price path in Figure 1.1 shows the percentage price change relative to the
excluded period (the thirteenth to sixteenth period before actual entry) for the twelve
quarters before entry to the twelve quarters after entry. According to Figure 1(c),
incumbents do not significantly change their average prices until Southwest Airlines
actually enters the route. Moreover, incumbents’ mean airfare steeply decreases in
the quarter of entry and the first quarter after entry. In fact, the solid line shows
that incumbents’ prices decrease 12.24% on average in the quarter following entry by
Southwest Airlines. Based on the 95% confidence intervals (the dotted lines), Figure
1(c) shows that this decrease is statistically significant. This key result corroborates
the previous findings in the literature. Namely, incumbents decrease their prices in
18
response to entry by Southwest Airlines. However, I want to determine whether this
effect is induced by other low-cost carrier entrants.
Further examination of the other price paths in Figure 1.1 shows that incumbents
tend to decrease their mean airfares the quarter before entry, the quarter of entry,
and the quarter after entry. Southwest Airlines had the largest average entry effect,
with the aforementioned result of inducing incumbents to decrease prices by 12.24%,
on average, the quarter after actual entry. Other low-cost carriers had similar, yet
weaker effects. AirTran Airways induced a decrease of 10.81%, while incumbents
also reacted to entry by JetBlue Airways and Spirit Airlines, but only by a modest
amount of 5.57% and 5.36%, respectively. Nevertheless, each low-cost carrier induced
incumbents to decrease their prices before and after actual entry.
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.2: Legacy Carrier Incumbent Response to Entry: Mean Airfare
19
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.3: Low-Cost Carrier Incumbent Response to Entry: Mean Airfare
The results from Table 1.1 suggest that it is worthwhile to examine the variations
in the entry response of legacy carrier and low-cost carrier incumbents. Figure 1.2
shows the relative price response of legacy carrier incumbents to entry by low-cost
carrier, whereas Figure 1.3 shows the price response of low-cost carrier incumbents.
Figures 1.2 and 1.3 correspond with the regression results in Tables A.3 and A.4 in
Appendix A, respectively.
Based on the price paths in Figure 1.2, legacy carriers respond to entry by lowcost carriers by dramatically decreasing their average airfares. In fact, there is a
more pronounced price drop than the effect shown in Figure 1.1, which considers all
incumbents servicing the entered route when the entrant actually enters. Southwest
Airlines induces incumbents to decrease their average prices by 13.09%. However,
AirTran Airways induces an even stronger effect than that of Southwest as incumbents
20
cut their mean airfare by an average of 13.31% the quarter after AirTran Airways
actually enters a route. Entry by JetBlue Airways and Spirit Airlines invokes legacy
carrier incumbents to decrease their prices by 7.07% and 7.98%, respectively. All of
these effects are larger than their respective effect implied by Figure 1.1.
The existing literature focuses on the strong entry effect of Southwest Airlines.
Over the past decade, other low-cost airlines have entered the industry and are currently major carriers in the industry. JetBlue Airways and AirTran Airways demonstrate how other low-cost carriers can mirror the entry effects exhibited with Southwest Airlines. The upshot is that the Southwest Effect can no longer be considered as
a special case relevant to one particular airline, particularly as it pertains to legacy
carrier incumbents. Rather, the entry effect pertains to low-cost carriers in general.
Figure 1.3 shows that low-cost carrier incumbents do not significantly alter their
mean airfare when either a low-cost carrier enters the route. These price paths are
in stark contrast with Figure 1.2, where it was shown that legacy carrier incumbents
significantly decrease their mean price. Therefore, legacy carrier incumbents (Figure
1.2) react differently than low-cost carrier incumbents (Figure 1.3) in their response
to entry by a low-cost carrier.
The differing response by legacy carriers and low-cost carriers can be rationalized
by the frequency of price matching by low-cost carrier entrants. Recall that Table 1.1
shows that low-cost carrier entrants are likely to undercut legacy carrier incumbents,
yet match the price of low-cost carrier incumbents. The competitive effect story
predicts that incumbents would decrease their price after entry occurs in response
to an increase in price competition from the entrant, whereas the displacement story
would induce a price increase by the incumbent. The results support the claim that
21
the competitive effect story applies to legacy carrier incumbents; however, low-cost
carrier incumbents are not susceptible to either effect.
1.3.3
Incumbent Price Response to Entry: 10th Percentile
Airfare, 90th Percentile Airfare, and Gini Coefficient
Different passengers who fly on the same flight may pay markedly different fares.
As such, it is possible that entry by a low-cost carrier could affect the price distribution of airfares set by incumbent carriers. Borenstein and Rose [4] show that price
dispersion increases as routes become more competitive. The intuition is that entry
can induce incumbents to decrease their discount price (i.e. the 10th percentile airfare) to attract price-sensitive consumers, while keeping their full-fare price (i.e. the
90th percentile airfare) high, resulting in an increase in price dispersion. Gerardi and
Shapiro (2009) conclude that price dispersion actually decreases when there is more
competition in the route. The intuition here is that an increase in competition erodes
the incumbent carriers’ market power, which mitigates the ability for these airlines to
effectively price discriminate. Therefore, price dispersion is smaller in markets that
are more competitive. In this section, I discuss the effect of entry by low-cost carriers
on the incumbents’ price distribution of airfares.
The price paths in this section are constructed based on regression results using
either the logged 10th percentile airfare, logged 90th percentile airfare, or the log-odds
ratio of the Gini coefficient as the dependent variable. As in Gerardi and Shapiro
[13], the 10th percentile airfare is intended to control for the effect on discount tickets,
whereas the 90th percentile airfare proxies for full-fare prices. These two dependent
variables effectively account for changes at the tails of the price distribution. The
Gini coefficient measures the price dispersion of a carrier’s prices on a specific route
22
in a particular time period, and is between zero and one. Since the Gini coefficient
emphasizes the middle of the price distribution, a full analysis of the entry effect on
incumbents’ price distribution involves analyzing the effects on the tails as well.16
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.4: Legacy Carrier Incumbent Response to Entry: 10th Percentile Airfare
Figure 1.4 shows that legacy carrier incumbents slash their 10th percentile prices
immediately before and immediately after entry. In the quarter after Southwest Airlines actually enters a route, legacy carrier incumbents decreased their 10th percentile
prices by 11.56%, on average, relative to the excluded period (the thirteenth to sixteenth quarter before entry). Other low-cost entrants induced similar effects, with
legacy carrier incumbents dropping prices by an average of 8.09%, 7.49%, and 7.69%
16
See Gerardi and Shapiro [13] for a more in-depth discussion on the pros and cons of the Gini
coefficient.
23
when AirTran Airways, JetBlue Airways, and Spirit Airlines entered the route, respectively. These results suggest that legacy carrier incumbents significantly decrease
their discount prices in response to entry by a low-cost carrier.
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.5: Low-Cost Carrier Incumbent Response to Entry: 10th Percentile Airfare
The analysis from Section 1.3.2 shows that the response by legacy carrier incumbents differs from that of low-cost carrier incumbents, as far as changes to mean
airfare is concerned. Figure 1.5 shows that the low-cost carriers do not significantly
alter their 10th percentile prices in response to entry by a rival low-cost carrier. Just
as with mean airfares, the results for 10th percentile airfares show a stark contrast in
the response by low-cost carriers from that of legacy carriers to entry by a low-cost
carrier.
Figure 1.6 indicates legacy carrier incumbents decrease their full fare prices, on
average. Southwest Airlines induces legacy carrier incumbents to decrease their 90th
24
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.6: Legacy Carrier Incumbent Response to Entry: 90th Percentile Airfare
percentile prices by 14.86%, while legacy carriers decreased their 90th percentile price
by 14.68% and 14.05% in the quarter after actual entry by AirTran Airways and Spirit
Airlines, respectively. Interestingly, these effects are of similar magnitudes than that
on the 10th percentile prices. Full fare prices charged by legacy carriers decreased
by 3.35%, on average, in response to entry by JetBlue Airways. Although this is not
as strong as their effect on 10th percentile prices, entry by JetBlue Airways still put
downward pressure on the incumbents’ full fare prices.
The results of the effect of entry by a low-cost carrier on low-cost carrier incumbents’ full fare prices are illustrated in Figure 1.7. In contrast to the results for
legacy carrier incumbents, low-cost carriers do not strongly respond to entry. The
analysis on entry by Southwest Airlines continues to show the pronounced effect that
they have on incumbents’ prices. As with mean airfare and discount prices, low-cost
25
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.7: Low-Cost Carrier Incumbent Response to Entry: 90th Percentile Airfare
carrier incumbents do not alter their full fares in the same manner as legacy carrier
incumbents in response to entry by low-cost carriers.
In order to examine the overall effect of entry on the incumbent’s price distribution,
I calculated the log-odds ratio of the carrier’s Gini coefficient, which measures the
carrier’s price dispersion at the route level. It may be the case that there is price
polarization, which would cause the Gini coefficient to increase. I use the log-odds
ratio as the dependent variable in Equation 1.1 and plot the transformation of the time
dummies. Figures 1.8 and 1.9 can be interpreted as the evolution of the incumbent’s
price dispersion in the entered route over time.
Figures 1.8 and 1.9 show that the Gini coefficient for the prices set by legacy
carriers and low-cost carriers, respectively, do not significantly respond to entry by
26
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.8: Legacy Carrier Incumbent Response to Entry: Gini Coefficient
(a) Entrant: AirTran Airways
(b) Entrant: JetBlue Airways
(c) Entrant: Southwest Airlines
(d) Entrant: Spirit Airlines
Figure 1.9: Low-Cost Carrier Incumbent Response to Entry: Gini Coefficient
27
a low-cost carrier.17 Again, the percentage change in the Gini coefficient is relative
to the excluded period (the thirteenth to sixteenth period before entry). Recall that
legacy carrier incumbents decrease both their 10th percentile and 90th percentile
prices, on average, in response to entry by a low-cost carrier. Although the 90th
percentile prices decrease more than the 10th percentile prices, the total effect on
the Gini is negligible. In other words, the Gini coefficient for legacy carriers does
not significantly change immediately before and after entry because both tails of the
price distribution decrease. The mean average airfare decreases as well, indicating
downward pressure on the entire price distribution. On the other hand, low-cost
carriers do not significantly respond to entry by a rival low-cost carrier as there is no
effect on either the mean airfare, the 10th percentile airfare, or the 90th percentile
airfare. Consequently, there is no significant effect on price dispersion by low-cost
carrier incumbents.
Both Borenstein and Rose [4] and Gerardi and Shapiro [13] determine the effect
of competition on price dispersion by estimating regression models consisting of a
transformation of the Gini coefficient18 as the dependent variable, while the independent variables include a proxy for competition. These papers are interested in
the estimated sign and significance of the competition variables on price dispersion.
Their results suggest a significant, yet contrasting effect. Gerardi and Shapiro [13]
attribute their differing results to the fact that they use panel data, while Borenstein
and Rose [4] use cross-sectional data. They argue that the results in Borenstein and
17
As a robustness check, I run the two-way fixed effects regression model using the interquartile
range of prices as the dependent variable. This serves as a robustness check for the Gini coefficient since the interquartile range would provide further information about the shape of the price
distribution. The results support the analysis on the Gini coefficient.
18
Borenstein and Rose [4] use logged Gini coefficient as their dependent variable, while Gerardi
and Shapiro [13] use the log-odds ratio of the Gini coefficient.
28
Rose [4] suffer from omitted-variable bias, which they fix by including route-carrier
fixed effects. I find that the Gini coefficient does not significantly change due to entry
by a low-cost carrier, implying that increased competition from entry by low-cost
carriers has no effect on price dispersion. Thus, the results of this paper differ from
the key findings in Borenstein and Rose [4] and Gerardi and Shapiro [13]. Borenstein
and Rose [4] find that price dispersion increases when there is more competition. This
would occur if entry induces incumbents respond to entry by decreasing their 10th
percentile prices, while keeping their 90th percentile prices high, suggesting that the
path for the Gini coefficient should be significantly positive around the time of entry.
However, Gerardi and Shapiro [13] find that an increase in competition would lead
to a decrease in price dispersion. My results would have corroborated their finding
if the path for the Gini coefficient was negative around the time of entry, suggesting
that an increase in competition due to the entry by a low-cost carrier would induce a
higher degree of price equality.
The identification strategy used in this paper is different than the strategy used
by Borenstein and Rose [4] and Gerardi and Shapiro [13], which could explain for
the dissimilar result. My approach is similar to an event study, in which I identify
individual events of entry and estimate the immediate effect of entry on incumbents’
prices. I examine how incumbents react differently to different low-cost carrier entrants around the time of actual entry. I also analyze how the incumbent response
differs depending on whether the incumbent is a low-cost carrier and legacy carrier.
However, Borenstein and Rose [4] and Gerardi and Shapiro [13] are interested in a
more general industry-wide effect of competition on incumbent prices. According to
Borenstein and Rose [4], competition is affected by a change in the Herfindahl Index
29
or the total number of flights on the route, whereas Gerardi and Shapiro [13] identify
a change in competition by a change in the Herfindahl Index or the total number of
carriers servicing the route. The results in this paper show that price dispersion does
not significantly change immediately following an increase in competition, specifically
when a low-cost carrier enters a new route.
1.4
Conclusion
This paper studies the incumbent response to entry by low-cost carriers. Legacy
carrier incumbents tend to decrease their average airfare, discount fares, and full fare
price before and after entry by a low-cost carrier. However, low-cost carriers do not
significantly alter their prices in response to entry by a rival low-cost carrier. In both
cases, the Gini coefficient does not significantly change, implying that entry does
not affect the incumbent’s price dispersion. This paper extends upon the work by
Goolsbee and Syverson [16] by going further to identify how incumbents respond to
entry by not only Southwest Airlines, but also other prominent low-cost carriers. The
key punch line to this paper is that although the strongest entry response occurs when
Southwest Airlines enters a new route, legacy carrier incumbents tend to respond in
a similar, yet weaker fashion to other low-cost carriers.
The results suggest that competition does not induce an immediate impact on
price dispersion. Entry by a low-cost carrier induces legacy carrier incumbents to
decrease their 10th percentile, 90th percentile, and mean airfares. Since legacy carrier
incumbents decrease prices all along the price distribution, then there was no net
change to the overall dispersion of prices. Low-cost carrier incumbents do not alter
their price dispersion as they do not significantly respond to entry by a rival low-cost
30
carrier. These findings extend the results in Borenstein and Rose [4] and Gerardi
and Shapiro [13], who focus on the effect of competition on price dispersion in the
industry as a whole.
Legacy carrier incumbents react differently to entry by low-cost carriers than lowcost carrier incumbents. Low-cost carrier entrants tend to undercut legacy carrier
incumbents, while matching the prices of low-cost carrier incumbents. Legacy carriers
decrease their prices in response to the low prices set by a low-cost carrier entrant.
This downward pressure on prices was not experienced by low-cost carrier incumbents
due to the weak price competition that ensued between rival low-cost carriers. This
paper sheds light on a previously unknown phenomena: the strategic interaction
between low-cost carrier entrants and rival low-cost carrier incumbents.
31
Chapter 2: The Influence of Low-Cost Carriers on the Use of
Regional Airlines
Legacy carriers have recently become more reliant on regional airlines as an important feeder of passengers within their route network. In fact, the number of passengers
flown by regional airlines has increased from 6.56 million in 1998 to 36.5 million in
2009, which is largely due to a growth in outsourcing by legacy carriers during this
time period. Under these arrangements, the planes are owned by the regional airlines,
but are painted to resemble the legacy carrier’s fleet. Pilots and flight attendants are
employed by the regional airline, yet the legacy carrier is responsible for ticketing and
operations at the airport. Legacy carriers contract with regional airlines primarily
because of their cost advantage.19 This paper examines the factors that contribute
to the “make-or-buy” decision regarding the operation of a route. In particular, I
investigate how low-cost carriers influence whether legacy carriers operate a route
themselves or outsource to a regional airline.
I focus on two potential explanations that could contribute to the growing use of
regional airlines. Legacy carriers might outsource to regional airlines as a response to
current competition with low-cost carriers. As a result of lower operating costs, legacy
carriers may find it easier to decrease average airfare once they switch the operation of
19
Hirsch [19] found that senior pilots and flight attendants at United Airlines make 80 percent
more and 32 percent more than their counterparts at regional airlines, respectively.
32
a route from their own fleet to a regional airline. Thus, a major factor in the growing
use of regional airlines could be motivated by increased price competition with existing
low-cost carriers. Alternatively, a legacy carrier might switch to a regional airline in
an attempt to erect a barrier to entry against prospective low-cost carriers.20 By
using a regional airline, legacy carriers could signal that a previously attractive route
would no longer be profitable to a prospective low-cost carrier entrant. The purpose
of this paper is to study the role of low-cost carriers on the decision of where to use
regional airlines.
My findings are largely consistent with the idea that regional airlines are used to
help legacy carriers better compete with low-cost carriers. Legacy carriers are more
likely to start using regional airlines on routes where low-cost carriers are present.
When the switch to regional airlines occurs, the legacy carrier typically lowers average
price to match that of competing low-cost carriers. In contrast, I find no evidence
that the likelihood of entry by low-cost carriers is reduced where legacy carriers have
outsourced to regional airlines. Therefore, the evidence is consistent with the notion
that outsourcing is a competitive pricing response to current competition with lowcost carriers rather than an attempt to preclude future entry by prospective low-cost
carriers.
Other studies have examined competitive responses to low-cost carriers. Goolsbee and Syverson [16] find that incumbents decrease their price prior to entry by
Southwest Airlines, a low-cost carrier. Gerardi and Shapiro [13] suggest that legacy
carriers experience a decrease in their ability to price discriminate when facing more
20
Forbes and Lederman [10] mention that outsourcing the operation of a route to a regional airline
could serve as an effective barrier to entry to low-cost carriers. Similarly, Borenstein [3] conjectures
that partnerships between legacy carriers and regional airlines can increase the cost of entry at
airports where the two airlines connect.
33
competition, especially from low-cost carriers. However, the existing literature has
not formally studied the use of regional airlines as a potential strategic response to
competition from low-cost carriers. By examining how low-cost carriers influence
where legacy carriers decide to use regional airlines, this paper contributes to the
literature on strategic interaction and competition in the U.S. airline industry.
2.1
Industry Structure
Before the Airline Deregulation Act of 1978, regional airlines operated as commuter airlines, servicing thin and short-haul routes. At the time, the Civil Aeronautics Board heavily regulated price and entry in the airline industry. Airlines were
allowed to set high prices on long-haul routes, which cross-subsidized profit losses
made on low-margin short-haul routes. Regional airlines, however, were exempt from
regulation as long as their fleet contained planes below a certain size.21 As such, they
operated independently from the major airlines at the time.
After the airline industry became deregulated in 1978, legacy carriers22 altered
their route structure by developing hub-and-spoke networks, in which passenger traffic
is concentrated through certain airports in the United States. Under this system,
legacy carriers have to decide whether to operate a route themselves or outsource to
a regional airline.23 Although passengers purchase their ticket from the legacy carrier,
the contracted regional airline supplies the aircrew and fleet used in the operation of
21
The size limit effectively limited regional airlines to planes with 20 to 30 seats.
22
Legacy carriers get their name from the fact that they have existed prior to deregulation.
23
Another recent trend is called codesharing, in which a legacy carrier operates a route on behalf
of a rival legacy carrier. See Goetz and Shapiro [15] for a detailed analysis on codesharing.
34
the flight. Legacy carriers typically use regional airlines on short-haul routes linking
a legacy carrier’s spoke airport to its hub airport, and vice versa.
Legacy carriers have become more attracted to outsourcing to regional airlines in
part because regional airlines have changed the type of aircraft they fly from turboprops to regional jets, which has increased range, speed, and passenger capacity. As
a result, the use of regional airlines by legacy carriers has drastically expanded over
time as the total number of routes that legacy carriers outsourced to regional airlines
increased from 1,917 in 1998 to 17,111 in 2009. Figure 2.1 shows that 213 million
and 6.56 million passengers flew on flights operated by legacy carriers and regional
carriers, respectively, in 1998. This corresponds to a market share of 67.3% for legacy
carriers and 2.1% for regional airlines. While the number of passengers flown by
legacy carriers decreased to 146 million passengers (44.7% market share) in 2009,
36.4 million passengers (11.1% market share) flew with a regional airline operating
on behalf of a legacy carrier, an increase of nearly 455% over the twelve year period.
Although legacy carriers continue to increase the amount of routes that are outsourced to regional airlines, this relationship has been partly restricted due to “scope
clauses” in the labor agreements between legacy carriers and labor unions. These
clauses limit the number of planes that can be operated by a regional airline on
behalf of the legacy carrier. Therefore, legacy carriers are faced with a trade-off
when they decide whether to switch to a regional airline as this would take away the
opportunity of using that regional airline on a different route.
The rise of low-cost carriers is another outcome of a deregulated airline industry.
Since deregulation made it easier for new airlines to enter the industry, start-up
airlines emerged that found ways to lower the cost of available seat mile relative to
35
Figure 2.1: Number of Passengers Flown by Operating Carrier
The number of passengers is calculated based on whether the operating carrier was a regional airline, low-cost
carrier, or legacy carrier. As such, the number of passengers is not determined by the ticketing carrier. For example,
regional airlines would be credited for the number of passengers it flew on behalf of a legacy carrier. Legacy carriers
only get credit for passengers who flew on flights that they operated themselves.
that of legacy carriers. These low-cost carriers decreased the cost of operation by
using a point-to-point network, non-unionized labor, and a fleet consisting of the
same aircraft.24 Legacy carriers established low-cost “airline within an airline”25 in
order to counter the influx of new airlines, but they have all since been discontinued
because they quickly became a financial burden to the legacy carrier.
As with regional airlines, low-cost carriers have experienced a remarkable growth
in the number of passengers flown between 1998 and 2009. Figure 2.1 illustrates that
the number of passengers flown by low-cost carriers has increased dramatically from
24
Southwest Airlines, for example, operates only Boeing 737 planes, which decreases the cost of
maintenance and inventory.
25
Examples include Continental Lite (Continental Airlines), Song (Delta Air Lines), and Ted
(United Airlines).
36
58.5 million (18.5% market share) in 1998 to 130 million (39.8% market share) in 2009.
During this time period, there have been 940 instances of entry26 by low-cost carriers
into routes with a maximum distance of 1,500 miles, with AirTran Airways entering
424 routes, JetBlue Airways entering 136 routes, Southwest Airlines entering 322
routes, and Spirit Airlines entering 58 routes. The expansion of the low-cost carrier’s
route network largely explains their recent growth and their emergence as a major
influence in the U.S. airline industry. This paper studies how price competition with
low-cost carriers could induce legacy carriers to outsource the operation of a route to
regional airlines.
2.2
Data
In order to investigate how legacy carriers use regional airlines to better compete
with low-cost carriers, I use data from three main sources. The main dataset used in
this paper is the Airline Origin and Destination Survey (DB1B), which is published
quarterly by the Bureau of Transportation Statistics. It is a ten percent survey of
domestic air travel and contains data on the origin, destination, non-stop distance
between endpoints, ticketing and operating carrier,27 market fare,28 and number of
passengers paying a particular market fare. I augment this data with monthly data
on the number of delayed flights from the Airline On-Time Performance Data set,
26
Each instance is defined by entry into a one-way airport pair. For example, when Southwest
started operating the route between Detroit Metro Airport and Philadelphia International Airport
in 2004, two routes are entered: the route from Philadelphia to Detroit, and the route from Detroit
to Philadelphia.
27
The key distinction between the ticketing carrier and the operating carrier is that the ticketing
carrier is the airline that the passenger purchased the ticket from, whereas the operating carrier is
the airline that is in charge of the aircrew and fleet used on the flight.
28
Market fare is calculated by the Bureau of Transportation Statistics as the itinerary yield multiplied by the number of miles flown. Other charges, such as baggage fees, priority seating fees, or
the cost of food and beverage purchased on the flight, are not accounted for in the market fare.
37
also from the Bureau of Transportation Statistics. Finally, I use yearly data from the
Local Area Personal Income tables on population, per capita personal income, and
personal income by major source and earnings at the metropolitan statistical arealevel, which are created and distributed by the Bureau of Economic Analysis. I use
the personal income by major source and earnings dataset to obtain information on
both accommodation and nonfarm earnings for metropolitan statistical areas (MSA).
Data from 1998 to 2009 are collected from each of the three data sources.
The following steps are undertaken to clean the data. First, I eliminate all observations where the distance was equal to zero or the market fare is less than $10.
Observations with an unidentified ticketing carrier are also dropped. Only observations related to nonstop flights are kept. I then limit the sample to routes within
the continental United States with a maximum distance of 1,500 miles since regional
airlines would not be used on longer routes and restrict the sample to the 2,500 routes
with the highest number of passengers from 1998 to 2009. This effectively removes
observations on routes involving airports in Alaska, Hawaii, and Puerto Rico. I drop
routes that are never serviced by a legacy carrier in order to focus on routes where
there is the potential for strategic behavior between legacy carriers29 and low-cost
carriers.30 In some cases, data on the number of delayed flights or accommodation
earnings are not reported. Routes with incomplete information on either of these two
variables are eliminated. The data used in this paper are discussed in greater detail
in Appendix B.
29
The legacy carriers studied in this paper are American Airlines, Continental Airlines, Delta Air
Lines, Northwest Airlines, United Airlines, and US Airways.
30
The low-cost carriers studied in this paper are AirTran Airways, JetBlue Airways, Southwest
Airlines, and Spirit Airlines.
38
2.3
Motivations for Regional Airline Entry
This section analyzes two possible mechanisms through which competitive pressures from low-cost carriers could influence the decision to outsource to regional airlines: 1) outsourcing as a response to current competition with low-cost carriers and
2) outsourcing to erect a barrier to entry against low-cost carriers. I examine the first
explanation by testing whether legacy carriers are more likely to switch to a regional
airline on routes where low-cost carriers are present. This is done using a two-way
fixed effects logit regression of entry by regional airlines on the number of low-cost
carriers operating on a route, as well as other control variables. Second, I estimate
the effect of regional airlines on entry by low-cost carriers in order to test whether
the presence of a regional airline reduces the likelihood that low-cost carriers enter
that route. In other words, I investigate whether legacy carriers outsource to regional
airlines where they intend to preclude future entry by regional airlines.
Observations from the DB1B and the Airline On-Time Performance Data are
aggregated to the year level so that the final dataset contains route-year observations.
Entry is identified when an airline starts servicing a route and remains on that route
for at least two consecutive quarters. In some cases, airlines are seen in the DB1B
to operate on a particular route only to drop out for a quarter and reappear in
the subsequent quarter. This is not an example of actual entry but represents an
issue with the DB1B being a ten percent sample of airline tickets. Nevertheless, this
problem was resolved by qualifying entry when the carrier did not service the route in
question for at least four quarters before the identified quarter of entry. The carrier
must have also flown at least 100 passengers on the entered route in the quarter of
39
entry. The final dataset contains 12,790 observations on 1,161 routes from 1998 to
2009.
There are three types of control variables: market variables, demographic variables, and competition variables. Market variables include the natural log of the
number of passengers on a route (lndensity) and the percentage of flights on a route
that were delayed at least 15 minutes (pdelay). Demographic variables include the
maximum of the ratio of accommodation earnings to nonfarm earnings for each endpoint on a route (tourism), as well as the geometric mean of the population (pop) and
per capita income (income) of the MSA where the origin and destination airports are
located. Finally, I include competition variables to control for the maximum market
share of a servicing airline on that route (maxshare) and the route-level HerfindahlHirschman Index (HHIroute). I also control for the number of competing airlines by
including the number of legacy carriers (nLEG), low-cost carriers (nLCC), regional
airlines (nREG), and other airlines (nOT HER) operating on that route. Summary
statistics for this dataset and a detailed description on variable construction can be
found in Appendix B.
I use a fixed effects approach to exploit the panel structure of my data in order
to test whether the presence of low-cost carriers affect the likelihood that a legacy
carrier outsources to a regional airline while controlling for time-invariant, routespecific factors. I am interested in routes where legacy carriers have a choice to
operate with a regional airline or not so the data only includes routes where legacy
carriers compete at some point in the sample time period. Legacy carriers typically
employ a regional airline on routes that they previously operated themselves. Entry
in this sense is defined when a legacy carrier starts using a regional airline where it
40
had previously served as both the ticketing and operating carrier. Once a regional
airline has entered the route, the legacy carrier remains as the ticketing carrier, but
the regional airline becomes the operating carrier. Thus, I construct REGentry as a
dependent variable, which assumes the value of 1 when a legacy carrier switches to a
partnered regional airline, and 0 otherwise.
The specification for the two-way fixed effects logit regression model is as follows:
REGentryi,t+1 = γi + νt + αXi,t + βnLCCi,t + i,t ,
(2.1)
where REGentryi,t+1 is the indicator variable that identifies entry by a regional airline, γi is the route fixed effect, νt is the year fixed effect, nLCCi,t is the number of
low-cost carriers operating on route i in year t, and Xi,t are the other control variables
explained above. Note that the control variables are in terms of period t, whereas the
dependent variable relates to period t + 1. In other words, I am looking at the effect
that the control variables in a particular year will have on entry by regional airlines
in the subsequent year. I am particularly interested in the sign and significance of
the nLCC variable, which controls for the number of low-cost carriers operating on
the route. If regional airlines are more likely to be used where low-cost carriers are
present, then the estimated coefficient for nLCC should be positive and statistically
significant.
Table 2.1 reports the results of the two-way fixed effects logit model. The estimated coefficient for nLCC 31 (0.363) is both positive and significant at the 1%
level. The estimates show that legacy carriers tend to start using regional airlines on
routes where low-cost carriers are present. The results also show that the presence
31
Alternatively, I used an indicator variable that is equal to 1 if any low-cost carrier operates on
the route and 0 otherwise. The results using this regional presence variable are qualitatively similar.
41
of other regional airlines on the route discourages legacy carriers to start using a
regional airline themselves as the estimated coefficient for nREG (-0.354) is negative
and statistically significant. Finally, outsourcing would be more likely to occur when
the route experiences a high frequency of delay (pdelay) and when the routes connect
markets with smaller populations (pop). This is likely to be the case since regional
airlines are used to integrate small cities into the legacy carrier’s route network. As a
robustness check, I ran logit regressions that includes route-invariant variables, such
as distance, hub airports, multi-airport markets, and slot-controlled airports, in lieu
of route fixed effects. The results for these specifications, which can be found in
Appendix C, are qualitatively similar.
Table 2.1: Entry by Regional Airlines
Dependent variable
Variable
Market density (lndensity)
Airport congestion (pdelay)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Route concentration (HHIroute)
Maximum market share (maxshare)
Number of legacy carriers (nLEG)
Number of low-cost carriers (nLCC)
Number of regional airlines (nREG)
Number of other airlines (nOT HER)
N
REGentry
Logit
Standard
coefficient
error
-0.003
(0.081)
1.836**
(0.395)
-0.751*
(0.299)
-0.464
(0.317)
-0.131
(0.125)
-0.686
(0.788)
0.033
(0.842)
-0.005
(0.062)
0.363**
(0.117)
-0.354**
(0.030)
0.064
(0.045)
418
Note: This table presents the results for the two-way fixed effects logit regression model on entry by regional
airlines. Entry is defined when a legacy carrier switches operation of a route to a regional airline. Observations are
at the route-year level. Route and year fixed effects suppressed.
* indicates significance at 5% level.
** indicates significance at 1% level.
42
The results suggest that legacy carriers are more likely to use regional airlines
where they currently compete with low-cost carriers. However, it could also be the
case that legacy carriers outsource to regional airlines on routes where they intend to
deter future entry by low-cost carriers. The rest of this section investigates whether
regional airlines could serve as an effective barrier to entry to low-cost carriers.
Previous papers have estimated barriers to entry using a logit model. Cotterill
and Haller [7] find that the number of large supermarket chains in a particular market
serves as an effective barrier to entry. Cetorelli and Strahan [6] conclude that banks
with market power erect a significant financial barrier to entry. These papers generally
use entry in the relevant market as a dependent variable and test whether particular
market conditions affect entry rates. If a logit coefficient for a particular variable
is negative and statistically significant, then that variable is determined to be an
effective barrier to entry.
In order to test whether regional airlines serve as a barrier to entry to low-cost
carriers, I utilize LCCentry, an indicator variable equal to 1 when a low-cost carrier
enters the route in the following year, and 0 otherwise, as the dependent variable in
a two-way fixed effects logit regression model with the following specification:
LCCentryi,t+1 = γi + νt + αXi,t + βnREGi,t + i,t ,
(2.2)
where LCCentryi,t+1 is the indicator variable that identifies entry by a low-cost carrier, γi is the route fixed effect,32 νt is the year fixed effect, nREGi,t is the number of
regional airlines on route i in year t, and Xi,t are the other control variables explained
32
The route fixed effect captures several variables that could potentially affect entry by low-cost
carriers, including distance of the route, the use of at least of the endpoints as the ticketing carrier’s
hub airport, the existence of substitute airports in a given MSA, or the usage of an airport slot
policy. Running logit models that explicitly control for these variables yield qualitatively similar
results, which are presented in Appendix C.
43
above. Note that the control variables are in terms of period t, whereas the dependent
variable relates to period t + 1. In other words, I am looking at the effect that the
control variables in a particular year will have on entry by low-cost carriers in the
subsequent year. I am particularly interested in the estimated sign and significance of
the nREG variable, which accounts for the number of regional airlines operating on
the route. If regional airlines serve as an effective barrier to entry to low-cost carriers,
the estimated coefficient for nREG should be negative and statistically significant.
Table 2.2 reports the regression results. The control variable of interest is nREG,33
which is the number of regional airlines operating on the route. The logit coefficient
for nREG (-0.016) is negative, yet statistically insignificant, implying that regional
airlines have no effect on entry by low-cost carriers. Thus, the results suggest that
legacy carriers are unable to preclude entry by a low-cost carrier by outsourcing to
regional airlines.
The regression results point to three significant factors to low-cost carrier entry.
Both the natural log of the number of passengers on the route (lndensity) and the
percentage of delayed flights (pdelay) have a negative and significant effect, implying
that low-cost carriers generally tend to avoid congested routes that are likely to cause
disruptions to their route network. Moreover, a higher number of low-cost carriers
operating on a route (nLCC) significantly inhibits rival low-cost carriers from entering
a route. That is not to say that low-cost carrier presence completely blocks entry. In
fact, there are some cases where low-cost carriers enter a route despite the presence of
a rival low-cost carrier. Nevertheless, the results suggest that this type of competition
discourages a possible low-cost carrier entrant from actually entering the route.
33
Alternatively, I used an indicator variable that is equal to 1 if any regional airline operates on
the route and 0 otherwise. The results using this regional presence variable are qualitatively similar.
44
Table 2.2: Entry by Low-Cost Carriers (Main Results)
Dependent variable
Variable
Market density (lndensity)
Airport congestion (pdelay)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Route concentration (HHIroute)
Maximum market share (maxshare)
Number of legacy carriers (nLEG)
Number of low-cost carriers (nLCC)
Number of regional airlines (nREG)
Number of other airlines (nOT HER)
N
LCCentry
Logit
Standard
coefficient
error
-0.622*
(0.271)
-3.691**
(1.155)
-0.193
(0.724)
-0.802
(0.971)
-0.060
(0.210)
-0.112
(2.011)
-1.200
(2.184)
-0.081
(0.175)
-4.280**
(0.298)
-0.016
(0.091)
0.239
(0.123)
3,597
Note: This table presents the results for the two-way fixed effects logit regression model on entry by four low-cost
carriers (AirTran Airways, JetBlue Airways, Southwest Airlines, and Spirit Airlines). Observations are at the
route-year level. Route and year fixed effects suppressed.
* indicates significance at 5% level.
** indicates significance at 1% level.
Industrial organization economists have been interested in Southwest Airlines as a
case study on the effect of low-cost carriers in the airline industry. Southwest Airlines
has a reputation of not being susceptible to aggressive entry deterrence strategies
by incumbents.34 Consequently, it could be the case that the results from Table
2.2 are largely influenced by Southwest Airlines. In order to check that other lowcost carriers are similarly unaffected by regional airlines, I run separate regressions
with either entry by Southwest Airlines (W N entry)35 or by other low-cost carriers
(otherLCCentry) as the dependent variable.
34
Bamberger and Carlton [1] find that Southwest Airlines has a high survival rate, meaning that
Southwest Airlines successfully remains on an entered route for at least a year after entry.
35
WN is the IATA code for Southwest Airlines.
45
Table 2.3: Entry by Southwest Airlines vs. Other Low-Cost Carriers
Dependent variable
Variable
Market density (lndensity)
Airport congestion (pdelay)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Route concentration (HHIroute)
Maximum market share (maxshare)
Number of legacy carriers (nLEG)
Number of low-cost carriers (nLCC)
Number of regional airlines (nREG)
Number of other airlines (nOT HER)
N
W N entry
Logit
Standard
coefficient
error
0.430
(0.737)
-3.559
(3.044)
-4.569
(2.598)
1.633
(2.739)
2.072*
(0.885)
-7.875
(5.505)
3.150
(6.213)
0.299
(0.459)
-6.422**
(1.144)
-0.246
(0.268)
0.218
(0.377)
869
otherLCCentry
Logit
Standard
coefficient
error
-0.809*
(0.340)
-3.916**
(1.267)
-1.489
(0.845)
-2.285*
(1.089)
-0.361
(0.276)
3.253
(2.283)
-2.505
(2.423)
-0.242
(0.201)
-3.561**
(0.306)
0.038
(0.102)
0.266
(0.148)
2,992
Note: This table presents the results for the two-way fixed effects logit regression models on entry by both
Southwest Airlines and other low-cost carriers (AirTran Airways, JetBlue Airways, and Spirit Airlines).
Observations are at the route-year level. Route and year fixed effects suppressed.
* indicates significance at 5% level.
** indicates significance at 1% level.
The results in Table 2.3 suggest that certain factors have heterogenous effects
on entry by Southwest Airlines and other low-cost carriers. In fact, some factors
that affect entry by Southwest Airlines do not affect entry by other low-cost carriers, and vice-versa. Southwest Airlines is more apt to enter routes in touristy markets (tourism), while other low-cost carriers are not inclined to enter dense routes
(lndensity) that are prone to delays (pdelay). The only determinant that significantly
affects both Southwest Airlines and other low-cost carriers is the number of low-cost
carriers operating on the route (nLCC), which suggests that the presence of low-cost
carriers significantly deters entry by low-cost carriers, in general.
46
The key insight from Table 2.3 is that regional airlines do not have an effect on
either Southwest Airlines or other low-cost carriers. The logit coefficients for the
number of regional airlines operating on a route (nREG) are statistically insignificant in both regressions. It is not surprising that Southwest Airlines is unaffected by
the presence of regional airlines given its reputation of being undeterred by incumbent legacy carriers’ aggressive pricing strategies. It is interesting, however, to see
that other low-cost carriers are unaffected as well. These coefficients provide further
evidence that regional airlines serve as ineffective barriers to entry to low-cost carriers.
Regional airlines and low-cost carriers compete against each other on certain
routes. For example, both United Airlines and Southwest Airlines fly nonstop between Seattle-Tacoma International Airport and Portland International Airport, yet
United Airlines uses a regional airline, SkyWest Airlines, to operate that route. However, these two types of airlines do not service all of the same markets. There are
routes that regional airlines service that low-cost carriers would consider too thin to
be profitable. These routes typically link a small spoke airport with one of the legacy
carrier’s hub airports, such as when Delta Air Lines uses Atlantic Southeast Airlines
to operate the route between Hartsfield-Jackson Atlanta International Airport and
Little Rock National Airport. No low-cost carriers service this route. On the other
hand, low-cost carriers operate on some routes where the distance between the two
endpoints is too far for the aircraft used by regional airlines. United Airlines uses
its own fleet and aircrew to operate the route between Baltimore/Washington International Airport and Denver International Airport, where it also competes with
Southwest Airlines. Thus, certain routes are more likely than others to be serviced
by both regional airlines and low-cost carriers.
47
Legacy carriers could focus their use of regional airlines on routes where they
anticipate entry by low-cost carriers instead of on routes where low-cost carriers are
less likely to operate. If this strategy decreases the attractiveness of that route to
a low-cost carrier, then both regional airlines and low-cost carriers would seemingly
be attracted to some of the same routes, resulting in an upward biased coefficient
for nREG. In order to resolve this potential endogeneity issue, I isolate a selected
sample of routes that are most likely to be served by both low-cost carriers and
regional airlines. By focusing on cases where regional airlines would have its highest
potential effect as a barrier to entry to low-cost carriers, I am able to investigate
whether regional airlines make attractive routes unattractive. In order to do this, I
start by truncating the full sample to keep routes which are serviced by at least one
low-cost carrier and one regional airline in every year of the sample period (1998 2009). I report the summary statistics (mean, 25th percentile, and 75th percentile)
for the full sample and the truncated sample in Table 2.4.
The summary statistics in Table 2.4 report that low-cost carriers and regional
airlines are both particularly attracted to routes that are affected by three variables:
distance, secondary airports, and slot-controlled airports. First, low-cost carriers
and regional airlines can both be found competing against each other on short-haul
routes. The mean and interquartile range for the distance of the route, distance, in the
truncated sample are both much lower than that in the full sample. In fact, the 75th
percentile distance in the truncated sample is nearly equivalent to the 25th percentile
distance in the full sample. Some metropolitan areas are serviced by multiple airports.
Brueckner, Lee, and Singer [5] identify the primary airport and secondary airport
48
Table 2.4: Summary Statistics (Truncated Sample)
All Routes
Variable
Route distance (distance)
Hub airport (hub)
Multi-airport market (multiapt)
Primary airport (primaryapt)
Secondary airport (secondaryapt)
Slot-controlled airport (slots)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Airport congestion (pdelay)
Mean
723
0.737
0.589
0.461
0.181
0.136
3.402
3.582
1.745
0.202
25th
Percentile
440
0
0
0
0
0
1.867
3.181
0.546
0.142
Routes with Both Low-Cost
Carriers and Regional Airlines
75th
25th
75th
Percentile Mean Percentile Percentile
988
425
308
493
1
0.779
1
1
1
0.357
0
1
1
0.357
0
1
0
0
0
0
0
0
0
0
4.255
2.380
1.130
3.595
3.946
3.444
3.085
3.822
1.211
2.483
0.502
1.036
0.252
0.172
0.119
0.217
Note: This table reports the summary statistics for all routes and for routes where at least one low-cost carrier and
one regional airline were present throughout the entirety of the sample period (1998-2009).
in these multi-airport markets.36 Using their airport definitions, I construct two
indicator variables that identify if either endpoint on the route is a primary airport
(primaryapt) or a secondary airport (secondaryapt). If either airport is classified
as either a primary airport or a secondary airport, then it is also coded to service a
multi-airport city (multiapt). To be sure, it can be the case that neither endpoint is
in a multi-airport market.37 According to the summary statistics, no routes in the
truncated sample appear to be identified as a secondary airport. In other words, lowcost carriers and regional airlines would never be found to simultaneously operate
routes to or from a secondary airport. Finally, the summary statistics for slots,
which is an indicator variable that assumes the value of 1 if either endpoint is a
slot-controlled airport, is different between the truncated sample and the full sample.
36
For example, the city of Dallas contains two airports: Dallas/Fort Worth International Airport
(primary airport) and Dallas Love Field (secondary airport).
37
In this case, primaryapt, secondaryapt, and multiapt for the observation would all equal 0.
49
Slot-controlled airports regulate the number of takeoffs and landings that airlines are
allowed each hour.38 Table 2.4 shows that no route with an endpoint that uses a slot
allocation would be serviced by both a low-cost carrier and a regional airline.
I use the interquartile range for distance, secondaryapt, and slots to set the
parameters for the selected sample incorporating the most attractive routes for both
low-cost carriers and regional airlines. I start with the full dataset, but only keep
the routes that fulfill three criteria: 1) the route must have a distance between 300
and 500 miles, 2) neither endpoint of the route can be a secondary airport, and 3)
neither endpoint of the route can be a slot-controlled airport. Using this new selected
sample, I then run the two-way fixed effects model outlined in Equation 2.2 using
entry by low-cost carriers (LCCentry) as the dependent variable. As with before, I
am interested in the estimated sign and significance of the nREG variable.
Table 2.5 presents the regression estimates for the selected sample that includes
routes in which the potential effect of regional airlines as a barrier to entry would be
at its highest. The results suggest that low-cost carriers seem to shy away from dense
markets (lndensity) with high per capita income (income), which reflects the trend
that smaller, less affluent markets tend to offer attractive operational conditions,
including lower operating costs. Most importantly, the number of regional airlines
operating on a route (nREG) has a negative, yet statistically insignificant effect on
entry by low-cost carriers. Moreover, the number of low-cost carriers operating on
the route (nLCC) is one of the variables that seems to affect low-cost carrier entry.
38
The four major airports using slot allocation in the United States include Chicago O’Hare
International Airport, New York LaGuardia Airport, New York John F. Kennedy International
Airport, and Ronald Reagan Washington National Airport.
50
Therefore, it is the presence of rival low-cost carriers, not regional airlines, that could
deter entry by low-cost carriers.
Table 2.5: Entry by Low-Cost Carriers (Selected Sample)
Dependent variable
Variable
Market density (lndensity)
Airport congestion (pdelay)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Route concentration (HHIroute)
Maximum market share (maxshare)
Number of legacy carriers (nLEG)
Number of low-cost carriers (nLCC)
Number of regional airlines (nREG)
Number of other airlines (nOT HER)
N
LCCentry
Logit
Standard
coefficient
error
-2.641*
(1.307)
0.311
(6.741)
3.553
(5.060)
-14.925*
(7.518)
0.500
(0.782)
6.530
(9.589)
-5.686
(10.301)
2.019*
(0.902)
-5.729**
(1.498)
0.550
(0.453)
0.146
(0.549)
418
Note: This table presents the results for the two-way fixed effects logit regression model on entry by four low-cost
carriers (AirTran Airways, JetBlue Airways, Southwest Airlines, and Spirit Airlines) using the selected sample. The
data truncates the full sample using three criteria: 1) the route distance is between 300 and 500 miles, 2) neither
endpoint airport is slot-controlled, and 3) neither endpoint airport is a secondary airport. Observations are at the
route-year level. Route and year fixed effects suppressed.
* indicates significance at 5% level.
** indicates significance at 1% level.
This specification corrects for a possible selection bias that arises when legacy
carriers focus their use of regional airlines on routes where low-cost carriers are most
likely to enter. Moreover, it is in this subsample of routes where regional airlines
would exhibit the most potential to serve as an effective barrier to entry. If regional
airlines are found to have no effect even in these markets, then the presence of regional
airlines would not seem to inhibit low-cost carriers from entering a route. The results
51
suggest that the use of regional airlines does not significantly reduce the likelihood of
entry by low-cost carriers. Thus, legacy carriers do not seem to be able to effectively
use regional airlines to preclude future entry by low-cost carriers.
Although it seems plausible that a low-cost carrier might find a route with regional
airline operation to be unattractive, the regression results in Tables 2.2, 2.3, and 2.5
suggest that this is not the case. Legacy carriers who outsource the operation of a
route to a regional airline do not inhibit low-cost carriers from entering that particular
route in the future. In fact, the regression results consistently suggest that the biggest
factor that could deter low-cost carrier entry is the existence of a rival low-cost carrier.
The upshot is that legacy carriers do not seem to effectively use regional airlines as a
barrier to entry to low-cost carriers.
The results from Table 2.1 imply, however, that legacy carriers use regional airlines as a response to competition against existing low-cost carriers. Legacy carriers
are more likely to start using regional airlines on routes where low-cost carriers are
present. Thus, legacy carriers may respond to current competition with low-cost carriers by using regional airlines, which could subsequently alter their pricing strategies.
2.4
The Effect of Regional Airline Entry on Pricing
This section investigates how outsourcing to regional airlines alters the pricing
strategy of the outsourcing legacy carrier and competing low-cost carriers. Legacy
carriers could potentially charge lower prices by exploiting the regional airlines’ more
efficient cost structure, thus allowing them to better compete with low-cost carriers.
Low-cost carriers might respond to this change in competition by changing their
average airfares once a legacy carrier switches to a regional airline. I run separate
52
two-way fixed effects regressions on logged average airfares in order to study price
changes associated with regional airline entry. I attempt to rationalize the regression
results by examining the frequency that legacy carriers price match competing lowcost carriers before and after outsourcing occurs.
Observations from the DB1B for every quarter between 1998:Q1 to 2009:Q4 is
merged with the annual population estimates of metropolitan statistical areas (MSA)
in the United States for every year between 1998 to 2009 from the Bureau of Economic
Analysis. The population for the airport’s MSA is assumed to be constant for each
quarter in a particular year. An observation in the resulting dataset is at the routecarrier-year-quarter level. Thus, the main distinction between this dataset and the
ones used in the previous section is that observations are at the route-carrier-yearquarter combination here, whereas the dataset used for the entry models is aggregated
to the route-year level.
I construct one dependent variable and seven control variables to be used in the
price regressions. The dependent variable (lnprice) is the logged average airfare set
by the ticketing carrier - operating carrier combination for a particular route in a
given year-quarter time period. I create two market share variables called msroute
and msapt, which calculated the market share of the carrier on a particular route and
the arithmetic mean of the carrier’s market share at the endpoint airports on a particular route in a given year-quarter, respectively. I proxy for market concentration
by constructing the route’s Herfindahl-Hirschman Index in a particular year-quarter
(HHIroute) and the arithmetic mean of the Herfindahl-Hirschman Indexes at endpoints on a route in a given time period (HHIapt). Both market share variables
and market concentration variables are based on the number of passengers flown by a
53
given ticketing carrier. In order to control for market size, I use the population data to
create pop, which is the geometric mean of the endpoint’s MSA population in millions.
Finally, I construct the two variables of interest: REGoperating and REGcompeting.
REGoperating assumes the value of 1 the year-quarter that a legacy carrier starts
to outsource to a regional airline and every subsequent year-quarter that the regional
airline operates on behalf of that legacy carrier, and 0 otherwise. REGcompeting assumes the value of 1 the year-quarter that a low-cost carrier competes against a legacy
carrier that switches to a regional airline and every subsequent year-quarter that the
low-cost carrier competes against the regional airline / legacy carrier partnership, and
0 otherwise.
I create two subsamples that isolate observations relevant to legacy carriers and
low-cost carriers. In the legacy carrier subsample, I keep observations that pertain to a
legacy carrier being the ticketing carrier and a regional airline as the operating carrier.
I then keep observations if the legacy carrier decided to start using the regional airline
partner as the operating carrier on the route during the sample time period (1998:Q1 2009:Q4). This subsample contains 91,777 observations on 1,313 routes from 1998:Q1
to 2009:Q4. In the low-cost carrier subsample, I keep observations where a low-cost
carrier serves as the ticketing carrier and was an incumbent when the legacy carrier
switched to a regional airline during the sample time period. This subsample contains
25,220 observations on 590 routes from 1998:Q1 to 2009:Q4. Summary statistics and
details on variable creation can be found in Appendix B.
By exploiting the panel structure of the legacy carrier subsample, I use a fixed
effects approach to perform an event study that measures the change in the legacy carrier’s price once it outsources to a regional airline while controlling for time-invariant
54
and route-specific factors. As such, the dependent variable used is the legacy carrier’s
logged average airfare (lnprice). The specification is as follows:
lnpriceij,t = γij + νt + αXij,t + βREGoperatingij,t + ij,t ,
(2.3)
where lnpriceij,t is the average airfare for legacy carrier i on route j in time t, γij is
the route-carrier fixed effect, νt is the year-quarter fixed effect, REGoperatingij,t is
the indicator variable used to identify when a legacy carrier i switches the operation
of a route to a regional airline on route j in time period t or prior to time period t,
and Xij,t are the other control variables discussed above. Standard errors are clustered by route-carrier to account for correlation between a route-carrier combination
over time. REGoperating, the key variable of interest, captures the change in price
charged when a regional airline operates on a route relative to what the legacy carrier charged when it previously operated the route itself. Given that regional airlines
have a more efficient cost structure than legacy carriers, the sign for REGoperating
is hypothesized to be negative and significant, implying that legacy carriers decrease
their average prices once they outsource the operation of a route to a regional airline,
on average.
Table 2.6 summarizes the price regression results that estimates how legacy carriers change their average price once they switch to regional airlines. The coefficient for
REGoperating (-0.111) is both negative and significant at the 1% level, which implies
that legacy carriers decrease their average price by 10.5% (exp(-0.111) - 1 = -0.105),
on average, when they outsource a route to a regional airline. This is consistent with
the idea that outsourcing allows the legacy carrier to charge lower prices, which may
allow it to better compete with rival carriers on that route.
55
Table 2.6: Price Response to Outsourcing
Type of Carrier
Legacy Carrier
Low-Cost Carrier
Standard
Standard
Variable
Coefficient
error
Coefficient
error
Route-level market share (msroute)
0.007
(0.020)
-0.091**
(0.033)
Route concentration (HHIroute)
0.203**
(0.021)
-0.145**
(0.029)
Airport-level market share (msapt)
-0.138*
(0.067)
0.406**
(0.103)
Airport concentration (HHIapt)
0.210**
(0.075)
-0.082
(0.097)
Population (pop)
0.364**
(0.096)
-0.093
(0.103)
Operates using regional airline (REGoperating)
-0.111**
(0.006)
–
–
Competes with regional airline (REGcompeting)
–
–
0.004
(0.005)
N
91,777
25,220
Note: This table reports the results of the two-way fixed effects price regressions outlined in Equations 2.3 and 2.4.
Observations are at the route-carrier-year-quarter level. Route and year-quarter fixed effects suppressed. Standard
errors are clustered by route-carrier to account for correlation between a route-carrier combination over time.
* indicates significance at 5% level.
** indicates significance at 1% level.
Competing low-cost carriers could respond to the lower price that legacy carriers charge once they switch to a regional airline by altering its pricing strategy as
well. In order to study how low-cost carriers react to outsourcing by legacy carriers, I run a separate two-way fixed effects regression using REGcompeting in lieu of
REGoperating. This second price regression uses the low-cost carrier subsample to
estimate how a low-cost carrier responds to a legacy carrier switching from operating
a route itself to outsourcing to a regional airline while controlling for time-invariant
and route-specific variables. As such, the dependent variable used here is the low-cost
carrier’s logged average airfare, lnprice. The specification is as follows:
lnpriceij,t = γij + νt + αXij,t + βREGcompetingij,t + ij,t ,
(2.4)
where lnpriceij,t is the average airfare for low-cost carrier i on route j in time t, γij
is the route-carrier fixed effect, νt is the year-quarter fixed effect, REGcompetingij,t
is the indicator variable used to identify when a low-cost carrier i competes with a
56
legacy carrier that switches the operation of a route to a regional airline on route j
in time period t or prior to time period t, and Xij,t are the other control variables
discussed above. Standard errors are clustered by route-carrier to account for correlation between a route-carrier combination over time. REGcompeting assumes the
value of 1 if a low-cost carrier competes against a legacy carrier that switches to a
regional airline and every subsequent year-quarter that the low-cost carrier competes
against the regional airline / legacy carrier partnership, and 0 otherwise. As such, a
positive (negative) and statistically significant coefficient for REGcompeting would
imply that low-cost carriers increase (decrease) their average price in response to a
competing legacy carrier outsourcing to a regional airline.
Table 2.6 reports the results for the price regression pertaining to low-cost carriers.
The coefficient for REGcompeting is statistically insignificant, suggesting that the
pricing behavior of low-cost carriers does not systematically change when competing
legacy carriers switches from operating the route itself to outsourcing to a regional
airline. This result is consistent with the key findings in Tan [32], which finds that
incumbent low-cost carriers do not significantly alter prices once a rival low-cost
carrier enters a route. One interpretation is that low-cost carriers treat competing
regional airlines as if they were a rival low-cost carrier.
In order to further examine these price responses, I look at the likelihood that
legacy carriers price match competing low-cost carriers. I define price matching as
a legacy carrier charging a price within 20% of a low-cost carrier’s price.39 Tables
2.7 and 2.8 report the frequency that legacy carriers match the price of a competing
39
I check for the sensitivity of the results by using different price windows. The results for these
robustness checks are reported in Appendix C.
57
low-cost carrier in the quarter before they outsource to a regional airline40 and in the
quarter that the switch occurs, respectively.
Legacy Carrier
Legacy Carrier
Legacy Carrier
Table 2.7: Frequency of Price Matching Before Outsourcing
Legacy Carrier’s Price > Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
28 (52.8%) 2 (100.0%) 24 (30.0%) 2 (100.0%)
Continental 12 (75.0%)
–
9 (30.0%)
–
Delta
146 (53.5%) 13 (52.0%) 54 (29.3%)
0 (0.0%)
Northwest
72 (63.2%)
–
41 (50.0%) 8 (100.0%)
United
33 (70.2%) 9 (90.0%) 29 (46.8%) 2 (100.0%)
US Airways 67 (59.3%) 4 (33.3%) 47 (70.1%) 14 (100.0%)
Legacy Carrier’s Price = Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
20 (37.7%)
0 (0.0%)
49 (61.3%)
0 (0.0%)
Continental
2 (12.5%)
–
21 (70.0%)
–
Delta
108 (39.6%) 12 (48.0%) 123 (66.8%)
0 (0.0%)
Northwest
31 (27.2%)
–
37 (45.1%)
0 (0.0%)
United
14 (29.8%) 1 (10.0%) 24 (38.7%)
0 (0.0%)
US Airways 44 (38.9%) 6 (50.0%) 15 (22.4%)
0 (0.0%)
Legacy Carrier’s Price < Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
5 (9.4%)
0 (0.0%)
7 (8.8%)
0 (0.0%)
Continental
2 (12.5%)
–
0 (0.0%)
–
Delta
19 (7.0%)
0 (0.0%)
7 (3.8%)
1 (100.0%)
Northwest
11 (9.6%)
–
4 (4.9%)
0 (0.0%)
United
0 (0.0%)
0 (0.0%)
9 (14.5%)
0 (0.0%)
US Airways
2 (1.8%)
2 (16.7%)
5 (7.5%)
0 (0.0%)
Note: This table reports the frequency and percentages of the occurrence that a legacy carrier sets an average price
that is higher than, similar to, or lower than that of incumbent low-cost carriers in the quarter prior to when a
legacy carrier switches the operation of a route to a regional airline. A dash signifies that the legacy carrier never
switched to a regional airline when the low-cost carrier exists on the route. A window of 20% defines price matching
when the legacy carrier’s average price is within 20% of the price charged by the low-cost carrier.
40
The results still hold even if looking at one year before outsourcing instead of just one quarter
prior to the switch.
58
Legacy Carrier
Legacy Carrier
Legacy Carrier
Table 2.8: Frequency of Price Matching After Outsourcing
Legacy Carrier’s Price > Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
16 (30.2%)
0 (0.0%)
17 (21.3%) 2 (100.0%)
Continental
9 (56.3%)
–
8 (26.7%)
–
Delta
145 (53.1%) 1 (4.0%)
23 (12.5%)
0 (0.0%)
Northwest
68 (59.6%)
–
32 (39.0%) 7 (87.5%)
United
24 (51.1%) 8 (80.0%) 28 (45.2%) 2 (100.0%)
US Airways 73 (64.6%) 6 (50.0%) 37 (55.2%) 13 (92.9%)
Legacy Carrier’s Price = Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
35 (66.0%) 1 (50.0%) 54 (67.5%)
0 (0.0%)
Continental
4 (25.0%)
–
17 (56.7%)
–
Delta
117 (42.9%) 21 (84.0%) 142 (77.2%) 0 (0.0%)
Northwest
39 (34.2%)
–
41 (50.0%) 1 (12.5%)
United
21 (44.7%) 2 (20.0%) 25 (40.3%)
0 (0.0%)
US Airways 36 (31.9%) 6 (50.0%) 30 (44.8%)
1 (7.1%)
Legacy Carrier’s Price < Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
2 (3.8%)
1 (50.0%)
9 (11.3%)
0 (0.0%)
Continental
3 (18.8%)
–
5 (16.7%)
–
Delta
11 (4.0%)
3 (12.0%) 19 (10.3%) 1 (100.0%)
Northwest
7 (6.1%)
–
9 (11.0%)
0 (0.0%)
United
2 (4.3%)
0 (0.0%)
9 (14.5%)
0 (0.0%)
US Airways
4 (3.5%)
0 (0.0%)
0 (0.0%)
0 (0.0%)
Note: This table reports the frequency and percentages of the occurrence that a legacy carrier sets an average price
that is higher than, similar to, or lower than that of incumbent low-cost carriers in the quarter when a legacy carrier
switches the operation of a route to a regional airline. A dash signifies that the legacy carrier never switched to a
regional airline when the low-cost carrier exists on the route. A window of 20% defines price matching when the
legacy carrier’s average price is within 20% of the price charged by the low-cost carrier.
The before and after evidence shows that legacy carriers tend to decrease their
average price toward the lower price levels of competing low-cost carriers once they
outsource to a regional airline. Price matching is more frequent in the quarter that the
59
legacy carrier starts outsourcing to the regional airline (Table 2.8) relative to the quarter prior to the switch (Table 2.7). Moreover, the legacy carrier is less likely to charge
an average price that is more than 20% higher than that of the competing low-cost
carrier once it makes the switch. For example, Table 7 shows that American Airlines matched the price of AirTran Airways on 20 of 53 routes (37.7%) in the quarter
prior to American Airlines switching to one of their three regional airline partnerships
(American Eagle, American Eagle/Executive, or Chautuaqua Airlines).41 American
Airlines set an average price higher than AirTran’s average price by at least 20%
on 28 of 53 routes (52.8%), while undercutting AirTran Airways on 5 of 53 routes
(9.4%). However, Table 8 shows that American Airlines increased the frequency of
price matching AirTran Airways to 35 of 53 routes (66.0%) when American Airlines
outsourced to a regional airline. In fact, American Airlines decreased the amount of
times it charged a higher price than AirTran to only 16 of 53 routes (30.2%), while
undercutting AirTran on just 2 of 53 routes (3.8%) once they switched to a regional
airline. Thus, the results suggest that not only are legacy carriers more inclined to
decrease their price once they switch to a regional airline, but this price also tends
to match that of a competing low-cost carrier. Legacy carriers seem to exploit the
cost advantages of regional airlines in order to charge a lower price and better compete with existing low-cost carriers, who do not tend to subsequently alter their price
strategy.
In order to test for the significance of the increase in the frequency of price matching after outsourcing, I run two specifications of an OLS regression using the price
matching outcome as the dependent variable. The first specification uses observations
41
A full list of the regional airline partnerships can be found in Appendix B.
60
that pertain to the quarter prior to outsourcing and the quarter when outsourcing
occurs for each route where a legacy carrier switches the operation of the route to a
regional airline. The second specification serves as a robustness check by comparing
price matching outcomes one year before outsourcing, instead of the quarter immediately preceding outsourcing, with observations in the quarter that outsourcing occurs.
The general regression specification is as follows:
pricematchingijr,t = β1 AAijr,t + β2 COijr,t + β3 DLijr,t + β4 N Wijr,t
+ β5 U Aijr,t + β6 U Sijr,t + β7 outsourceijr,t + ijr,t ,
(2.5)
where pricematchingijr,t is equal to 1 when legacy carrier i price matches low-cost
carrier j on route r in time period t and 0 otherwise, AAijr,t , COijr,t , DLijr,t , N Wijr,t ,
U Aijr,t , and U Sijr,t are dummy variables that indicate whether the outsourcing legacy
carrier is American Airlines, Continental Airlines, Delta Air Lines, Northwest Airlines,
United Airlines, or US Airways, respectively, and outsourceijr,t is equal to 1 if legacy
carrier i outsources to a regional airline on route r in time period t and 0 otherwise.
Standard errors are robust and clustered by route - legacy carrier - low-cost carrier
combinations in order to account for intertemporal correlation between an outsourcing
legacy carrier and a competing low-cost carrier on a particular route. The coefficient
for outsource, the key variable of interest, can be interpreted as the change in the
share of price matching after outsourcing.
Table 2.9 presents the regression results for Equation 2.5. Column 1 reports the
regression results using the first specification, in which observations pertain to the
quarter prior to outsourcing and the quarter when outsourcing occurs. The estimated coefficient for outsource (0.065) is positive and statistically significant, implying that price matching occurs on 6.5% more routes, on average, once a legacy
61
Table 2.9: Price Matching by Legacy Carriers
(1)
(2)
Standard
Standard
Variable
Coefficient
error
Coefficient
error
American Airlines (AA)
0.560**
(0.035)
0.503**
(0.030)
Continental Airlines (CO)
0.446**
(0.068)
0.534**
(0.065)
Delta Air Lines (DL)
0.539**
(0.024)
0.498**
(0.024)
Northwest Airlines (N W )
0.330**
(0.034)
0.279**
(0.034)
United Airlines (U A)
0.329**
(0.041)
0.334**
(0.044)
US Airways (U S)
0.269**
(0.030)
0.295**
(0.032)
Occurrence of outsourcing (outsource)
0.065**
(0.017)
0.086**
(0.020)
N
2,137
2,003
Note: This table presents the results for the OLS regression model on price matching by legacy carriers outlined in
Equation 2.5. Price matching occurs when a legacy carriers sets an average price within 20% of a competing
low-cost carrier on a given route. Column (1) reports the results for the specification using observations that pertain
to the quarter prior to outsourcing and the quarter when outsourcing occurs, whereas column (2) reports the results
for the specification using observations that pertain to the year before outsourcing and the quarter when outsourcing
occurs. Observations pair an outsourcing legacy carrier with an existing low-cost carrier on a route in a particular
year-quarter. Standard errors are robust and clustered by route - legacy carrier - low-cost carrier combinations in
order to account for intertemporal correlation between an outsourcing legacy carrier and a competing low-cost
carrier on a particular route.
** indicates significance at 1% level.
carrier outsources the operation of a route to a regional airline relative to the quarter
immediately preceding outsourcing. Thus, legacy carriers are more inclined to price
match competing low-cost carriers once they start outsourcing to a regional airline.
This result still holds qualitatively for the second specification (Column 2),42 which
uses observations related to the year prior to outsourcing and the quarter that outsourcing occurs, since the estimated coefficient for outsource (0.086) is also positive
and significant. Moreover, the carrier dummies report the share of routes that are
price matched prior to outsourcing. In the quarter immediately preceding outsourcing (Column 1), American Airlines price match on 56% of routes where a low-cost
42
The second specification includes less observations than the first specification because some
routes experienced low-cost carrier entry within one year of outsourcing by legacy carriers to a
regional airline.
62
carrier exists, whereas United Airlines price match on 32.9% of routes. Based on the
results in Column 2, Delta Air Lines and US Airways price match competing low-cost
carriers on 49.8% and 29.5% of routes in the year prior to outsourcing, respectively.
These results confirm the statistical significance of the increase in the frequency of
price matching after outsourcing.
Overall my findings provide complementary insights to those of previous studies that have investigated how legacy carriers respond to competition from low-cost
carriers. Goolsbee and Syverson [16] find that airlines decrease prices when facing
potential competition from Southwest Airlines.43 In other words, airlines decrease
price even before the low-cost carrier services that route. The mere potential, and
likelihood, that Southwest Airlines will enter that route is sufficient for incumbent
carriers to respond by undercutting their price. Tan [32] uses similar methodology to
find that legacy carriers decrease their price when a low-cost carrier44 actually enters
the route. This paper shows that legacy carriers use regional airlines as a means to
lower prices in order to better compete with low-cost carriers.
2.5
Conclusion
Some firms face a “make-or-buy” decision to either produce a product internally or
outsource to an outside supplier. One reason that a firm would choose to outsource
is to reduce operating costs, especially if the benefit of the lower production cost
from outsourcing outweigh the transactions costs associated with search frictions and
43
Potential competition with Southwest Airlines occurs on a route from airport A to airport B
when Southwest Airlines flies to both airports, but does not service the direct route linking airport
A to airport B.
44
Tan [32] examines entry not only by Southwest Airlines, but also by three other low-cost carriers
(AirTran Airways, JetBlue Airways, and Spirit Airlines).
63
incomplete contracting. This issue is prevalent in the U.S. airline industry, where
legacy carriers decide to operate a route using their own fleet and aircrew or outsource
to a more cost-efficient regional airline.
This paper investigates how low-cost carriers influence where regional airlines are
used by legacy carriers. The results suggest that legacy carriers are more likely to
start outsourcing to regional airlines on routes where they currently compete with
a low-cost carrier. I also find that legacy carriers tend to decrease average prices
once they switch to a regional airline and that this lower price tends to match the
average airfare charged by existing low-cost carriers. However, the presence of a
regional airline does not serve as an effective barrier to entry. Therefore, I conclude
that legacy carriers use regional airlines as a response to current competition with
low-cost carriers and not as an attempt to preclude future entry by low-cost carriers.
Although regional airlines provide a more cost-efficient alternative to operating a
route themselves, legacy carriers are unable to use regional airlines on all routes. First,
regional airlines use smaller aircraft that can only carry between 50-100 passengers
at a time. As such, legacy carriers would not want to use regional airlines if the
distance is too far or if the demand for a particular route is too high. In these cases,
it would be more profitable for a legacy carrier to operate the route with their own
fleet and aircrew. Moreover, “scope clauses” in labor agreements with legacy carriers
limits the number of routes that can be outsourced to regional airlines. Despite these
limitations, regional airlines serve as a means for legacy carriers to better compete
with current competitors on certain routes.
Industrial organization economists have long been interested in pricing phenomenons,
particularly in the U.S. airline industry. Previous papers have found evidence that
64
airlines charge higher prices at their hub airport, that competition affects the ability
for airlines to price discriminate, and that some incumbent airlines decrease their
price in response to entry by a low-cost carrier, particularly Southwest Airlines. This
paper analyzes yet another facet of price competition between airlines by investigating how outsourcing to regional airlines enables legacy carriers to set a lower price
in order to better compete with existing low-cost carriers. It would be interesting to
study how the recent mergers between legacy carriers (e.g. Delta Air Lines - Northwest Airlines and United Airlines - Continental Airlines) affect their relationship with
their respective regional airline partners and how price competition adapts to the imminent change in the competitive environment. Alas, I leave this topic up for future
research.
65
Chapter 3: The Effect of De-Hubbing on Airfares
Hub-and-spoke networks have become the predominant route network structure
for legacy carriers since the U.S. airline industry deregulated in 1978. Under this
system, a legacy carrier moves passenger traffic between spoke airports through one of
its hub airports in order to exploit economies of scope and economies of traffic density.
Each of these airlines has several hub airports strategically located in different regions
of the United States.45 However, some of the legacy carriers have recently de-hubbed
an airport by ceasing hub operations at that airport. Delta Air Lines de-hubbed
Cincinnati/Northern Kentucky International Airport (CVG) in 2006 and Orlando
International Airport (MCO) in 2001, while US Airways and American Airlines dehubbed Reagan National Airport (DCA) in 2006 and Lambert-St. Louis International
Airport (STL) in 2004, respectively. This paper analyzes the impact of de-hubbing
on airfares at these four de-hubbed airports.
There is a dearth of academic research on de-hubbing, and what little has been
done has studied its effect on capacity. Redondi, Malighetti, and Paleari [29] formally define the criteria for identifying cases of de-hubbing and identify 37 airports
45
For example, American Airlines currently utilizes Dallas/Fort Worth International Airport, John
F. Kennedy International Airport, Los Angeles International Airport, Miami International Airport,
and O’Hare International Airport as hub airports within the United States.
66
that have been de-hubbed between 1997 and 2009 world-wide. They find that dehubbing, which can occur due to weak demand or a strategic decision to focus on other
nearby hub airports, results in a significant and permanent decrease in the number
of scheduled flights and seats offered. However, they do not take into consideration
the ramifications of de-hubbing on airfares. As such, this paper is the first to study
the effect of de-hubbing on airfares.
In contrast to the lack of attention spent on de-hubbing, the existing literature
has been focused on the hub premium, in which prices are higher, on average, when
at least one of the route’s endpoints is a hub airport for the servicing airline. Legacy
carriers experience more market power at their hub airports because passengers are
attracted to the higher frequency of flights and the increased variety of destinations
that they offer from the hub airport. Moreover, Lederman [25] finds that certain
passengers are willing to pay higher prices in order to receive future awards from the
airline’s frequent-flyer program. Early works empirically estimated the value of the
hub premium by regressing logged airfares on airport market shares, while controlling
for other factors. Borenstein [2] and Evans and Kessides [9] both find that airport
market shares has a positive and statistically significant effect on airfares. More
recently, Lee and Luengo-Prado [26] use hub dummy variables as a more explicit
proxy for the hub premium and find that prices are between 12.2% and 13.0% higher,
on average, when the flight travels to or from an airline’s hub airport. However, in
contrast to these studies which rely mainly on cross-sectional variation to identify a
hub premium, this paper utilizes variation over time in the hub status of particular
airports.46
46
I focus on de-hubbing because hub creation is usually too gradual.
67
Since de-hubbing leads to a reduction in the frequency of flights and number of
destinations serviced to and from the de-hubbed airport, two possible price effects
could occur. On the one hand, passengers could become less brand loyal to that
airline since they no longer dominate the flights to and from that airport. As such,
the airline’s local market power decreases, which could eliminate the hub premium.
Thus, de-hubbing could potentially lead to lower airfares as demand becomes more
elastic due to the unraveling of the airline’s market power at the de-hubbed airport.
On the other hand, the reduction in capacity by the de-hubbing airline could soften
price competition, assuming that rival airlines do not respond by altering their flight
schedules. If the de-hubbed airline’s competitors maintain a steady number of flights
and seats offered, then airfares could potentially increase even though the market is
less concentrated. This paper empirically tests these two stories using data from the
U.S. airline industry.
In order to analyze this pricing puzzle, I study four instances of de-hubbing at
domestic airports between 2001 and 2006. Using a difference-in-differences estimation
approach, I find a positive and statistically significant price increase after de-hubbing.
Moreover, I allow for the possibility that the de-hubbing airline might alter its pricing
strategy differently than its competitors at the de-hubbed airport. The results of this
difference-in-difference-in-differences estimation strategy implies that the de-hubbing
airline increases its average prices by a similar percentage as its competitors. Thus,
the key result of this paper is that de-hubbing softens price competition, which leads
to an increase in average airfares.
The results of this paper are somewhat surprising given the tremendous amount
of research on the rationale behind the existence of the hub premium. Despite the
68
removal of upward pressure on prices that are found at hub airports, prices further
elevate once that airport has been de-hubbed as the decrease in capacity leads to a
weaker competitive environment. The results shed light on the ramifications of an
interesting, yet relatively unexplored strategic decision by legacy carriers. Namely,
the pricing phenomenon that follows once an airline decides to stop using a particular
airport as a hub airport.
3.1
Data
I construct a dataset from three sources in order to test the effect of de-hubbing
on airfares. The main dataset used in this paper is the Airline Origin and Destination
Survey (DB1B), which is a 10% survey of domestic travel that is published quarterly
by the Bureau of Transportation Statistics. An observation in the raw data provides
information on the number of passengers who paid a certain price to fly with a
particular airline on a given route, as well as the distance between the two endpoints
of the route. Using this data, I am able to calculate both the mean average airfare and
the number of passengers for a particular airline on a given route in each year-quarter.
I also obtain data on the number of scheduled flights and available seats from the T100 database, which is also published by the Bureau of Transportation Statistics. The
T-100 dataset, which provides monthly traffic and capacity data of travel within the
United States by domestic airlines, is aggregated for a year-quarter combination to
the route-carrier level so that it can be merged with the DB1B. Finally, I augment this
dataset with yearly estimates on population and per capita income for metropolitan
statistical areas (MSA), which can be found in the Local Area Personal Income tables
69
that are created and distributed by the the Bureau of Economic Analysis. Data from
1993 to 2009 are collected from each of the three data sources.
I focus on coach class fares on nonstop or one-stop flights between the top 100
airports within the continental United States.47 Following Ito and Lee [21], I eliminate
any observations with a reported price that is less than $25 or more than $1500 as
these observations are believed to be either frequent-flyers or incorrectly coded. I also
drop any observations where the airline has a market share of less than 1% as is done
in Borenstein [2]. The resulting dataset consists of 1,517,163 observations on 8,158
routes. Summary statistics are summarized in Table 3.1.
Table 3.1: Summary Statistics
Variable
priceijt
distancej
passengersijt
nLEGit
nLCCit
popjt
incomeit
Routes
N
Definition
Mean
(Std. Dev.)
Average one-way market fare for carrier i on route j in time period t
200.36
(64.52)
One-way distance (in miles) between the endpoints of route j
1,262.85
(657.70)
Number of passengers for carrier i on route j in time period t
2,059.75
(5,922.27)
Number of legacy carriers operating route j in time period t
3.89
(1.55)
Number of low-cost carriers carriers operating route j in time period t
0.17
(0.41)
Geometric mean of population (in hundreds of thousands) of origin
25.66
and destination airports’ MSA on route j in time period t
(20.76)
Geometric mean of per capita income (in tens of thousands) of origin
3.33
and destination airports’ MSA on route j in time period t
(0.72)
Number of routes in the sample
8,158
Number of observations
1,517,163
47
Rankings are based on the number of boardings in 2009 and are obtained from the Federal
Aviation Administration. The airport with the most amount of boardings is Hartsfield-Jackson
Atlanta International Airport, while the 100th-ranked airport is Myrtle Beach International Airport.
70
(a) CVG (Delta Air Lines)
(b) DCA (US Airways)
(c) MCO (Delta Air Lines)
(d) STL (American Airlines)
Figure 3.1: Number of Flights by De-Hubbing Airline
The number of flights is the sum of the scheduled departure and arrival flights at the particular airport. The gray
dashed line indicates the year-quarter that the airport became de-hubbed by the airline identified in parentheses.
Redondi, Malighetti, and Paleari [29] identified 37 airports world-wide that were
de-hubbed between 1997 and 2009. Four of these airports are in the United States,
including Cincinnati/Northern Kentucky International Airport (CVG), Reagan National Airport (DCA), Orlando International Airport (MCO), and Lambert-St. Louis
International Airport (STL). I define the de-hubbing time period at these airports
as the year-quarter after the largest drop in the number of scheduled flights, which
is then verified using public sources. Figure 3.1 graphs the total number of flights
scheduled to and from each of these four airports for the de-hubbing airline between
71
1993 and 2009. The gray dashed line indicates the time period when the airport is
considered to become de-hubbed.
Figure 3.1 shows a sharp decline in the number of flights following de-hubbing at
each of the four airports. Delta Air Lines de-hubbed CVG in 2006:Q1 prior to their
merger with Northwest Airlines, which was announced in April 2008. In 2001:Q4,
Delta de-hubbed MCO, which served as the hub airport for Song, its currently defunct
subsidiary. Moreover, other legacy carriers also de-hubbed during this time period.
Upon acquiring Trans World Airlines (TWA) in 2001, American Airlines set up hub
operations at STL, where TWA had been headquartered. STL was supposed to alleviate the traffic congestion at Chicago O’Hare International Airport and Dallas/Fort
Worth International Airport, two of American’s other hub airports. However, the
merger resulted in a financial drain and American Airlines subsequently de-hubbed
STL in 2004:Q1. Finally, US Airways de-hubbed DCA in 2006:Q4 following their
merger with America West in 2005.48 All four examples of de-hubbing experienced a
stark decrease in capacity, which is reported in Table 3.2.
Table 3.2 presents two ways to study the effect of de-hubbing on quantity: the
total number of flights, which is the sum of the scheduled departure and arrival flights
at the airport, and the total number of seats, which is the sum of the seats offered
on all of those flights. In order to analyze the de-hubbing effect, I compare the time
period before de-hubbing, defined to be the three to six quarters prior to the airport
becoming de-hubbed, with the time period after de-hubbing, defined to be the three
to six quarters following de-hubbing. The percent change from these two time periods
48
There is a sharp decrease in the number of flights by US Airways to and from DCA in 2001:Q4. It
is believed that this is a response to a negative shock to demand following the September 11 attacks
and not a concerted de-hubbing effort as evidenced by a spike in capacity beginning in 2004:Q4.
72
STL
MCO
DCA
CVG
Table 3.2: Capacity Before and After De-Hubbing
All Airlines
Delta
Competitors
All Airlines
US Airways
Competitors
All Airlines
Delta
Competitors
All Airlines
American
Competitors
Total Number of Flights
Before
After
Percent
De-Hubbing De-Hubbing Change
108,695
44,296
-59.2%
106,544
42,803
-59.8%
2,151
1,493
-30.6%
159,000
135,120
-15.0%
76,344
53,627
-29.8%
82,656
81,493
-1.4%
209,031
182,093
-12.9%
64,787
37,699
-41.8%
144,244
144,394
0.1%
231,089
102,125
-55.8%
162,454
38,915
-76.0%
68,635
63,210
-7.9%
Total Number of Seats
Before
After
Percent
De-Hubbing De-Hubbing Change
15,398,463
6,802,906
-55.8%
15,168,678
6,633,355
-56.3%
229,785
169,551
-26.2%
19,339,702
17,539,441
-9.3%
8,584,479
6,829,585
-20.4%
10,755,223
10,709,856
-0.4%
30,571,678
28,154,311
-7.9%
9,807,581
6,562,253
-33.1%
20,764,097
21,592,058
4.0%
31,145,731
13,449,236
-56.8%
22,196,619
5,369,376
-75.8%
8,949,112
8,079,860
-9.7%
Note: This table reports changes in capacity by all airlines, the de-hubbing airline, and other airlines present at the
de-hubbed airport. The total number of flights is the sum of the scheduled departure and arrival flights at the
particular airport, whereas the total number of seats is the sum of the offered seats on all of those flights. The
before and after periods includes the the three to six quarters prior to and following de-hubbing, respectively. This
allows for a one-year excluded period around the de-hub date that accounts for the transitional time period in which
de-hubbing takes effect.
is also reported in the table. These two measures of quantity are summarized for all
airlines servicing the airport, which is composed of the de-hubbing airline and its
competitors. The results show that de-hubbing leads to an overall decrease in the
total number of flights and the total number of seats at each of these four airports; yet,
this is largely due to the fact that the de-hubbing airline substantially decreases its
operation at the airport, by definition. For example, the total number of flights and
seats offered by all airlines servicing MCO decreased by 12.9% and 7.9%, respectively,
while Delta Air Lines, the airline that de-hubbed MCO, reduced the number of flights
by 41.8% and the total number of seats offered by 33.1%. With the exception of CVG,
there is a relatively small change in capacity by competitors of the de-hubbing airline
after the airport has become de-hubbed. In fact, competitors at DCA decreased the
73
total number of scheduled flights and seats offered by 1.4% and 0.4% following dehubbing by US Airways. MCO is the only airport in which competitors increased their
capacity in response to de-hubbing, albeit by a relatively small amount. Regardless,
both proxies for quantity show that legacy carriers reduce capacity once they de-hub
an airport, while competing airlines generally maintain a steady level of capacity.
These results are not surprising in light of Redondi, Malighetti, and Paleari [29], who
find that operations are permanently scaled-back at de-hubbed airports. With this
in mind, I turn to analyzing the price effect of de-hubbing.
3.2
Empirical Analysis
In order to test the effect of de-hubbing on airfares, I conduct an event study on
four airports that were de-hubbed between 2001 and 2006. By using a difference-indifferences approach, I am able to infer an overall before and after effect of de-hubbing
on airfares. In order to determine whether the price response of the de-hubbing airline
differs from its competitors, I use a difference-in-difference-in-differences estimation
technique. The results from both estimation strategies suggest that average airfares
increase following de-hubbing. The following section formalizes the estimation strategy and discusses the regression results.
I use a two-way fixed effects model in order to yield a differences-in-differences
(DID) estimate on the effect of de-hubbing on airfares. The dependent variable is
logged average airfares (lnprice). I control for the geometric mean of the population
(pop) and per capita income (income) of the two endpoint airports’ MSAs, as well
the number of legacy carriers (nLEG) and low-cost carriers (nLCC) that service the
route. I also include a dummy variable (airport) that indicates whether the de-hubbed
74
airport is one of the route’s endpoint airports, another dummy variable (dehub) that
indicates whether the time period is pre- or post-de-hubbing, and the interaction term
of the two dummy variables (airport × dehub). The airport variable is constructed
to limit the data sample to a before and after period, where the before period starts
three quarters prior to the identified de-hubbing date and ends six quarters before dehubbing whereas the after period starts three quarters following the de-hubbing time
period and ends six quarters after de-hubbing. This allows for a one-year excluded
period around the de-hub date that accounts for the transitional time period in which
de-hubbing takes effect. The specification for the difference-in-differences regression
model is as follows:
lnpriceij,t = γij + νt + αXij,t + β1 airportij + β2 dehubij,t
+ β3 (airportij × dehubij,t ) + ij,t ,
(3.1)
where lnpriceij,t is the average one-way airfare for airline i on route j in time t, γij
is the carrier-route fixed effects, νt is the year-quarter fixed effects, airportij is the
airport dummy variable,49 dehubij,t is the de-hub time dummy variable, and Xij,t
are the other control variables explained above. I cluster the standard errors by
route-carrier in order to account for intragroup correlation over time. The variable of
interest is the interaction term (airport × dehub), which signifies the DID estimate.
A positive and statistically significant coefficient for the interaction term implies that
airfares increase, on average, after the airport has been de-hubbed.
Table 3.3 reports the results of the DID regression for each of the four de-hubbed
airports. The coefficient for airport×dehub, the control variable of interest, is positive
and statistically significant for all four de-hubbed airports, meaning that the DID
49
By construction, the airport dummy variable becomes absorbed by the route-carrier fixed effects.
75
Table 3.3: Difference-in-Differences Results
Variable
De-hub time dummy (dehub)
CVG
DCA
MCO
STL
0.163** 0.232** -0.155** -0.071**
(0.004)
(0.007)
(0.004)
(0.003)
Airport/de-hub interaction (airport × dehub) 0.289** 0.039** 0.047** 0.069**
(0.008)
(0.009)
(0.006)
(0.007)
Population (pop)
-0.260** -0.586** 0.661** 0.727**
(0.044)
(0.050)
(0.086)
(0.066)
Per capita income (income)
-0.351** -0.718** 0.960**
0.083
(0.053)
(0.058)
(0.051)
(0.060)
Number of legacy carriers (nLEG)
-0.006** -0.008** -0.009** -0.011**
(0.001)
(0.001)
(0.001)
(0.001)
Number of low-cost carriers (nLCC)
-0.023** -0.025** -0.110** -0.062**
(0.003)
(0.003)
(0.005)
(0.003)
N
194,570 190,970 183,915 190,799
Note: This table reports the results of the two-way fixed effects price regressions outlined in Equation 3.1.
Observations are at the route-carrier-year-quarter level. Route and year-quarter fixed effects suppressed. Standard
errors, which are presented in parentheses, are clustered by route-carrier to account for correlation between a
route-carrier combination over time.
* indicates significance at 5% level.
** indicates significance at 1% level.
method implies that average airfares at de-hubbed airports increases after it has
been de-hubbed by a legacy carrier. The results suggest that de-hubbing contributed
to a 3.9%, 4.7%, and 6.9% increase in average airfares at DCA, MCO, and STL,
respectively, yet the effect is more pronounced at CVG. Recall that Table 3.2 showed
that DCA, MCO, and STL experienced a substantial reduction in capacity by the
de-hubbing airline with little response by its competitors, whereas Delta Air Lines
and its competitors all substantially reduced their capacity once Delta de-hubbed
CVG. Thus, the strong effect at CVG can be attributed to the substantial capacity
changes by the de-hubbing airlines and its competitors. Nonetheless, the reduction
76
in capacity by the de-hubbing airline seems to soften competition in every case of
de-hubbing.
I specify a separate two-way fixed effects regression model in order to ascertain
whether the de-hubbing airline changed its prices at a different rate than its competitors once it de-hubs an airport. In order to yield a difference-in-difference-indifferences (DDD) estimate, I include three additional variables to Equation 3.1: a
dummy variable (carrier) that indicates the de-hubbing airline, the interaction term
(dehub × carrier) of the time dummy variable and the de-hubbing airline dummy
variable, as well as the triple interaction term (airport × dehub × carrier). The specification for the differences-in-differences-in-differences regression model is as follows:
lnpriceij,t = γij + νt + αXij,t + β1 airportij + β2 dehubij,t + β3 carrierij
+ β3 (airportij × dehubij,t ) + β4 (dehubij,t × carrierij )
+ β5 (airportij × dehubij,t × carrierij ) + ij,t ,
(3.2)
where lnpriceij,t is the average one-way airfare for airline i on route j in time t
and carrierij is equal to 1 if airline i de-hubs one of the endpoint airports on route
j.50 The dehub × carrier interaction term controls for any nationwide change in
price for airline i that occurs following de-hubbing. The interpretation of the other
variables follows directly from Equation 3.1. Given this specification, the estimate
for the airport × dehub interaction term represents the percent increase in average
airfares by competitors of the de-hubbing airline after the airport has been de-hubbed.
However, I am particularly interested in the airport×dehub×carrier interaction term,
which signifies the DDD estimate. A positive and statistically significant estimate for
50
By construction, carrier becomes absorbed by the route-carrier fixed effects.
77
airport × dehub × carrier implies that the de-hubbing airline increases its airfares at
a higher percentage than its competitors once it has de-hubbed an airport.
Table 3.4: Difference-in-Difference-in-Differences Results
Variable
De-hub time dummy (dehub)
CVG
0.165**
(0.004)
Airport/de-hub interaction (airport × dehub)
0.282**
(0.012)
De-hub/carrier interaction (dehub × carrier)
-0.009**
(0.003)
Airport/de-hub/carrier interaction (airport × dehub × carrier)
0.020
(0.016)
Population (pop)
-0.252**
(0.044)
Per capita income (income)
-0.344**
(0.053)
Number of legacy carriers (nLEG)
-0.006**
(0.001)
Number of low-cost carriers (nLCC)
-0.023**
(0.003)
N
194,570
DCA
MCO
STL
0.213** -0.164** -0.069**
(0.007)
(0.004)
(0.003)
0.026** 0.046** 0.051**
(0.009)
(0.007)
(0.008)
0.116** 0.052** -0.015**
(0.004)
(0.003)
(0.003)
0.021
0.010
0.069**
(0.027)
(0.012)
(0.017)
-0.542** 0.594** 0.727**
(0.050)
(0.086)
(0.066)
-0.669** 0.934**
0.098
(0.057)
(0.050)
(0.059)
-0.006** -0.009** -0.011**
(0.001)
(0.001)
(0.001)
-0.028** -0.110** -0.062**
(0.003)
(0.005)
(0.003)
190,970 183,915 190,799
Note: This table reports the results of the two-way fixed effects price regressions outlined in Equation 3.2.
Observations are at the route-carrier-year-quarter level. Route and year-quarter fixed effects suppressed. Standard
errors, which are presented in parentheses, are clustered by route-carrier to account for correlation between a
route-carrier combination over time.
* indicates significance at 5% level.
** indicates significance at 1% level.
Table 3.4 reports the results for the DDD estimation strategy for the four dehubbed airports. The airport × dehub interaction term is positive and significant for
all four de-hubbed airports, which implies that the de-hubbing airline’s competitors
increase average airfares after de-hubbing occurs. Moreover, the airport × dehub ×
carrier term accounts for the difference in airfares between the de-hubbing airline
and its competitors once of of the route’s endpoint airports becomes de-hubbed. The
estimate for airport × dehub × carrier is positive, yet statistically insignificant for
78
CVG, DCA and MCO, implying that the de-hubbing airline increases its airfares at
the same rate as its competitors after it has de-hubbed an airport. However, the
coefficient for airport × dehub × carrier for STL (0.069) is positive and statistically
significant, meaning that American Airlines further increased its average airfares by
6.9% relative to its competitors after it de-hubbed STL. The upshot of these regression
results is that the price increase is attributed not only to the de-hubbing airline but
also the other airlines servicing the de-hubbed airport.
The regression results discussed in this section shed light on the price puzzle
regarding de-hubbing on airfares. Although prices are found to increase after an
airline ceases hub operations at an airport, it would have been theoretically plausible
if prices decreased since de-hubbing results in a less concentrated market. Thus,
the results overwhelmingly support the story that the decrease in capacity by the
de-hubbing airline reduces the availability of substitutes at the de-hubbed airport,
which weakens competition and leads to higher average airfares.
3.3
Conclusion
Legacy carriers have de-hubbed certain airports due to changes in demand
and competitive environment. The potential effect of de-hubbing on prices is nontrivial. Airfares could decrease due to the market becoming less concentrated once
the dominant airline scales back its operation at the de-hubbed airport. However,
the reduction in the frequency and variety of flights diminishes the availability of
substitutes, which leads less competition and subsequent increase in price. By using
an event study estimation approach, this paper serves as the first attempt to test
79
which of these two stories characterizes the effect of de-hubbing on airfares in the
U.S. airline industry.
The results suggest that de-hubbing puts upward pressure on prices at de-hubbed
airports. Not only do average airfares increase after the airport becomes de-hubbed,
but the percent change in airfares is also shown to rise at a similar rate for both
the de-hubbing airline and its competitors. A possible explanation for this pricing
phenomenon is that competition is softened once an airport is de-hubbed as the dehubbing airline drastically reduces the number of scheduled flights and seats offered
whereas its competitors maintain a steady level of capacity.
Several extensions can be explored in future research. First, it is possible that
legacy carriers have de-hubbed other domestic airports. If this is true, then it would
be helpful to add these other examples to the empirical analysis. Second, studying
the effects of de-hubbing on the 10th and 90th percentile airfares could provide more
insight on how de-hubbing affects other sections of the price distribution. Finally, it
would be interesting to investigate whether low-cost carriers respond to de-hubbing
differently than legacy carriers. Nonetheless, the results in this paper serve as the
foundation for future empirical work on de-hubbing in the U.S. airline industry.
80
Appendix A: Tables for “Incumbent Response to Entry by
Low-Cost Carriers in the U.S. Airline Industry”
Table A.1: Summary Statistics
Variable
Definition
priceijt
Average one-way market fare for carrier i
on route j in time period t
Gini coefficient of carrier i’s prices
on route j in time period t
One-way distance (in miles) between the endpoints of route j
giniijt
distancej
passengersijt
mktshrrouteijt
Number of passengers for carrier i
on route j in time period t
Market share for carrier i on route j in time period t
HERF routejt
Herfindahl Index for route j in time period t
mktshraptijt
Arithmetic mean of carrier i’s market share
at endpoints on route j in time period t
Arithmetic mean of Herfindahl Indexes
at endpoints on route j in time period t
Geometric mean of population (in millions) of origin and
destination airports’ MSA on route j in time period t
Number of routes in the sample
Number of observations
HERF aptjt
popjt
Routes
N
81
Mean
(Std. Dev.)
170.35
(72.68)
0.249
(0.089)
1196.28
(663.10)
1014.65
(1641.07)
0.223
(0.275)
0.484
(0.189)
0.157
(0.128)
0.247
(0.079)
4.04
(2.43)
1000
263,272
Table A.2: Incumbent Price Response to Actual Entry
(1)
Entrant:
AirTran Airways
Incumbent:
All Carriers
t0 − 12
0.0139
(0.0072)
t0 − 11
0.0194*
(0.0076)
t0 − 10
0.0105
(0.0073)
t0 − 9
0.0107
(0.0080)
t0 − 8
-0.0155*
(0.0076)
t0 − 7
-0.0132
(0.0083)
t0 − 6
-0.0302*
(0.0089)
t0 − 5
-0.0320*
(0.0089)
t0 − 4
-0.0067
(0.0088)
t0 − 3
-0.0237*
(0.0090)
t0 − 2
-0.0224*
(0.0087)
t0 − 1
-0.0362*
(0.0092)
t0
-0.0691*
(0.0088)
t0 + 1
-0.1143*
(0.0102)
t0 + 2
-0.1274*
(0.0103)
t0 + 3
-0.1330*
(0.0095)
t0 + 4
-0.1131*
(0.0095)
t0 + 5
-0.1093*
(0.0095)
t0 + 6
-0.1184*
(0.0094)
t0 + 7
-0.1145*
(0.0094)
t0 + 8
-0.0872*
(0.0098)
t0 + 9
-0.0897*
(0.0093)
t0 + 10
-0.0887*
(0.0101)
t0 + 11
-0.0776*
(0.0098)
t0 + 12
-0.0663*
(0.0105)
(2)
JetBlue Airways
All Carriers
0.0040
(0.0137)
0.0080
(0.0135)
-0.0242
(0.0132)
-0.0408*
(0.0181)
-0.0209
(0.0152)
-0.0278*
(0.0134)
-0.0481*
(0.0171)
-0.0492*
(0.0149)
-0.0603*
(0.0181)
-0.0126
(0.0152)
-0.0048
(0.0157)
-0.0406*
(0.0200)
-0.0447*
(0.0153)
-0.0573*
(0.0155)
-0.1044*
(0.0168)
-0.0980*
(0.0157)
-0.0676*
(0.0165)
-0.0619*
(0.0177)
-0.0871*
(0.0170)
-0.0914*
(0.0152)
-0.0858*
(0.0146)
-0.0136
(0.0205)
-0.0121
(0.0205)
-0.0564*
(0.0223)
-0.0570*
(0.0239)
(3)
Southwest Airlines
All Carriers
0.0236*
(0.0100)
0.0183
(0.0107)
0.0367*
(0.0103)
0.0044
(0.0108)
-0.0019
(0.0110)
0.0059
(0.0100)
0.0326*
(0.0103)
0.0274*
(0.0104)
0.0337*
(0.0105)
0.0246*
(0.0110)
0.0262*
(0.0122)
-0.0017
(0.0109)
-0.0820*
(0.0112)
-0.1306*
(0.0120)
-0.1137*
(0.0119)
-0.1198*
(0.0130)
-0.1230*
(0.0121)
-0.1369*
(0.0120)
-0.1057*
(0.0115)
-0.1131*
(0.0118)
-0.1184*
(0.0122)
-0.1086*
(0.0125)
-0.0883*
(0.0118)
-0.1026*
(0.0123)
-0.0800*
(0.0120)
(4)
Spirit Airlines
All Carriers
0.0159
(0.0148)
0.0097
(0.0162)
0.0191
(0.0186)
-0.0059
(0.0167)
-0.0246
(0.0191)
-0.0395*
(0.0167)
-0.0275
(0.0186)
-0.0021
(0.0168)
-0.0056
(0.0156)
0.0101
(0.0156)
-0.0210
(0.0157)
-0.0433*
(0.0161)
-0.0365*
(0.0200)
-0.0551*
(0.0224)
-0.0344
(0.0224)
-0.0408*
(0.0204)
-0.0466*
(0.0182)
-0.0600*
(0.0203)
-0.0591*
(0.0191)
-0.0364
(0.0247)
-0.0577*
(0.0196)
-0.0653*
(0.0212)
-0.0782*
(0.0200)
-0.0717*
(0.0223)
-0.0467*
(0.0214)
Note: The dependent variable is the logged average airfare (lnprice). The variables shown are the lagged/forward
time dummies, where t0 is quarter of entry. Fixed effects, competition variables, and market variables suppressed.
Standard errors are in parentheses. Number of observations: 263,270.
* denotes significance at 5% level.
82
Table A.3: Legacy Carrier Incumbent Price Response to Actual Entry
(1)
Entrant:
AirTran Airways
Incumbent: Legacy Carriers
t0 − 12
0.0069
(0.0085)
t0 − 11
0.0080
(0.0088)
t0 − 10
-0.0021
(0.0086)
t0 − 9
0.0006
(0.0096)
t0 − 8
-0.0310*
(0.0087)
t0 − 7
-0.0360*
(0.0095)
t0 − 6
-0.0479*
(0.0103)
t0 − 5
-0.0478*
(0.0103)
t0 − 4
-0.0276*
(0.0095)
t0 − 3
-0.0440*
(0.0104)
t0 − 2
-0.0489*
(0.0093)
t0 − 1
-0.0644*
(0.0102)
t0
-0.0916*
(0.0096)
t0 + 1
-0.1428*
(0.0103)
t0 + 2
-0.1474*
(0.0109)
t0 + 3
-0.1564*
(0.0103)
t0 + 4
-0.1368*
(0.0102)
t0 + 5
-0.1309*
(0.0103)
t0 + 6
-0.1401*
(0.0101)
t0 + 7
-0.1405*
(0.0099)
t0 + 8
-0.1170*
(0.0102)
t0 + 9
-0.1124*
(0.0100)
t0 + 10
-0.1090*
(0.0110)
t0 + 11
-0.1014*
(0.0104)
t0 + 12
-0.0913*
(0.0106)
(2)
JetBlue Airways
Legacy Carriers
0.0003
(0.0152)
-0.0013
(0.0149)
-0.0290
(0.0160)
-0.0281
(0.0166)
-0.0144
(0.0147)
-0.0276
(0.0155)
-0.0523*
(0.0161)
-0.0620*
(0.0175)
-0.0552*
(0.0166)
-0.0138
(0.0174)
-0.0065
(0.0184)
-0.0295
(0.0209)
-0.0511*
(0.0173)
-0.0734*
(0.0166)
-0.1183*
(0.0188)
-0.1163*
(0.0171)
-0.0789*
(0.0181)
-0.0674*
(0.0193)
-0.0848*
(0.0182)
-0.0852*
(0.0170)
-0.0859*
(0.0167)
-0.0236
(0.0240)
-0.0149
(0.0242)
-0.0613*
(0.0259)
-0.0571*
(0.0283)
(3)
(4)
Southwest Airlines Spirit Airlines
Legacy Carriers
Legacy Carriers
0.0225
0.0123
(0.0123)
(0.0173)
0.0154
0.0117
(0.0130)
(0.0195)
0.0346*
0.0183
(0.0123)
(0.0196)
-0.0027
-0.0025
(0.0129)
(0.0192)
-0.0062
-0.0449*
(0.0130)
(0.0154)
0.0004
-0.0556*
(0.0117)
(0.0165)
0.0294*
-0.0417*
(0.0122)
(0.0189)
0.0250*
-0.0032
(0.0123)
(0.0169)
0.0396*
-0.0100
(0.0123)
(0.0170)
0.0241
0.0017
(0.0131)
(0.0173)
0.0324*
-0.0247
(0.0147)
(0.0181)
-0.0033
-0.0504*
(0.0125)
(0.0179)
-0.0925*
-0.0652*
(0.0130)
(0.0202)
-0.1403*
-0.0831*
(0.0139)
(0.0248)
-0.1188*
-0.0674*
(0.0139)
(0.0226)
-0.1227*
-0.0612*
(0.0151)
(0.0229)
-0.1185*
-0.0660*
(0.0142)
(0.0201)
-0.1253*
-0.0856*
(0.0136)
(0.0226)
-0.0974*
-0.0862*
(0.0132)
(0.0206)
-0.1086*
-0.0718*
(0.0139)
(0.0218)
-0.1170*
-0.0865*
(0.0138)
(0.0209)
-0.0999*
-0.0859*
(0.0146)
(0.0231)
-0.0928*
-0.0963*
(0.0136)
(0.0219)
-0.1104*
-0.0942*
(0.0144)
(0.0247)
-0.0823*
-0.0694*
(0.0140)
(0.0238)
Note: The dependent variable is the logged average airfare (lnprice). The variables shown are the lagged/forward
time dummies, where t0 is quarter of entry. Fixed effects, competition variables, and market variables suppressed.
Standard errors are in parentheses. Number of observations: 263,270.
* denotes significance at 5% level.
83
Table A.4: Low-Cost Carrier Incumbent Price Response to Actual Entry
(1)
Entrant:
AirTran Airways
Incumbent: Low-Cost Carriers
t0 − 12
0.1307*
(0.0132)
t0 − 11
0.1263*
(0.0167)
t0 − 10
0.1370*
(0.0219)
t0 − 9
0.0895*
(0.0249)
t0 − 8
0.0659*
(0.0305)
t0 − 7
0.0704*
(0.0252)
t0 − 6
0.0543*
(0.0215)
t0 − 5
0.0686*
(0.0286)
t0 − 4
0.0777*
(0.0292)
t0 − 3
0.1177*
(0.0257)
t0 − 2
0.0943*
(0.0234)
t0 − 1
0.0609*
(0.0233)
t0
0.1069*
(0.0233)
t0 + 1
0.1162*
(0.0232)
t0 + 2
0.1134*
(0.0254)
t0 + 3
0.0832*
(0.0360)
t0 + 4
0.1153*
(0.0310)
t0 + 5
0.0825*
(0.0309)
t0 + 6
0.0654*
(0.0307)
t0 + 7
0.0564
(0.0351)
t0 + 8
0.0881*
(0.0359)
t0 + 9
0.1224*
(0.0343)
t0 + 10
0.0847*
(0.0322)
t0 + 11
0.0912*
(0.0304)
t0 + 12
0.1506*
(0.0370)
(2)
JetBlue Airways
Low-Cost Carriers
-0.0110
(0.0400)
0.0457
(0.0452)
-0.0067
(0.0395)
-0.0422
(0.0449)
-0.0296
(0.0438)
-0.0592
(0.0405)
-0.1188*
(0.0475)
-0.0922*
(0.0395)
-0.0827
(0.0516)
-0.0981
(0.0573)
-0.1449*
(0.0460)
-0.1445*
(0.0471)
-0.0516
(0.0425)
-0.1139*
(0.0333)
-0.1070*
(0.0374)
-0.1225*
(0.0485)
-0.0741*
(0.0373)
-0.0709*
(0.0352)
-0.0870*
(0.0348)
-0.1541*
(0.0385)
-0.1190*
(0.0342)
0.0010
(0.0436)
0.0073
(0.0307)
-0.0221
(0.0350)
-0.1140*
(0.0440)
(3)
Southwest Airlines
Low-Cost Carriers
-0.0148
(0.0429)
-0.0950
(0.0572)
-0.0648
(0.0566)
-0.1020*
(0.0410)
-0.1083*
(0.0427)
-0.1203*
(0.0436)
-0.0890
(0.0456)
-0.1023*
(0.0467)
-0.1242*
(0.0490)
-0.0968
(0.0499)
-0.1387*
(0.0490)
-0.0776
(0.0631)
-0.0571
(0.0599)
-0.1040
(0.0545)
-0.0982*
(0.0497)
-0.1270*
(0.0551)
-0.2183*
(0.0446)
-0.1957*
(0.0454)
-0.2146*
(0.0575)
-0.1840*
(0.0500)
-0.1881*
(0.0464)
-0.2821*
(0.0467)
-0.1791*
(0.0474)
-0.1488*
(0.0468)
-0.1580*
(0.0469)
(4)
Spirit Airlines
Low-Cost Carriers
0.0511*
(0.0203)
0.0236*
(0.0116)
-0.0058
(0.0267)
0.0047
(0.0324)
0.0631*
(0.0306)
0.0434
(0.0398)
0.0341
(0.0456)
0.0691
(0.0454)
0.0767
(0.0419)
0.1004*
(0.0376)
0.0684
(0.0359)
0.0441
(0.0438)
0.0580
(0.0455)
0.0763*
(0.0297)
0.0365
(0.0311)
0.0293
(0.0431)
0.0452
(0.0306)
0.0829*
(0.0319)
0.0640
(0.0369)
0.0505
(0.0433)
0.0857
(0.0453)
0.0058
(0.0340)
0.0056
(0.0418)
0.0382
(0.0416)
0.0129
(0.0452)
Note: The dependent variable is the logged average airfare (lnprice). The variables shown are the lagged/forward
time dummies, where t0 is quarter of entry. Fixed effects, competition variables, and market variables suppressed.
Standard errors are in parentheses. Number of observations: 263,270.
* denotes significance at 5% level.
84
Appendix B: Data Construction for “The Influence of
Low-Cost Carriers on the Use of Regional Airlines”
I collect data from three sources. The first is the Airline Origin and Destination
Survey (DB1B), which is published quarterly by the Bureau of Transportation Statistics. Each observation in this dataset, which is a ten percent survey of domestic air
travel, provides information on the number of passengers who paid a particular price
to an identified ticketing carrier in order to fly on a certain route that is operated by
an identified operating carrier. The data also provides the distance of the route and
the number of coupons associated with the itinerary. I obtain data on the number of
delayed flights at the route level from the Airline On-Time Performance Data, which
is published monthly by the Bureau of Transportation Statistics. Finally, the Local
Area Personal Income tables, which is published annually by the Bureau of Economic
Analysis, provides annual estimates on population, per capita personal income, and
personal income by major source and earnings at the metropolitan statistical area
(MSA)-level. This section details the step-by-step process used in order to create the
main dataset used in this paper.
The sample time period for this paper is 1998 - 2009. I begin by downloading
all the DB1B files for 1998:Q1 to 2009:Q4. I start with 1998 because the Bureau of
Transportation Statistics did not ask carriers to report whether the operating carrier
85
differed from the ticketing carrier until that year, while 2009 is chosen because the
Delta Air Lines / Northwest Airlines merger was officially completed in 2010. By
using 2009 as a cutoff year, I am able to isolate a pristine time period before the
competitive nature of the airline industry was drastically altered. I then download
and merge the Airline On-Time Performance Data from January 1998 to December
2009, as well as the annual estimates in the Local Area Personal Income tables from
1998 to 2009.
The following steps enumerate the process taken of cleaning the data and constructing the variables (in italics) used in this paper:
1. I merge all of the DB1B files from 1998:Q1 to 2009:Q4.
2. I drop observations where either the ticketing carrier or operating carrier is
unidentified.
3. Following Gerardi and Shapiro [13], I drop observations where the market fare
was less than $10 in order to eliminate frequent-flyer tickets.
4. I drop observations if either endpoint is an airport in Alaska, Hawaii, or Puerto
Rico. Forbes and Lederman [12] justify this criteria since the nature of the
routes is different between flights within the continental United States and these
states and territories. Karlsson, Odoni, and Yamanaka [22] comment that routes
involving Alaska and Hawaii are subject to special, complex tax rules, which
alters the route structure to and from this area.
5. I keep observations pertaining to nonstop flights.
86
6. I generate msroute to be the ticketing carrier’s market share at the route-level
based on the number of passengers flown in a particular year-quarter.
• I generate maxshare to be the maximum market share of all ticket carriers
servicing a given route in a particular year-quarter.
7. I generate HHIroute to be the sum of squared market shares at the route-level
in a particular year-quarter.
8. Following Evans and Kessides [9], I drop observations where the ticketing carrier
had less than a 1% market share on the route.
9. Following Forbes and Lederman [11], I limit the data to routes with a maximum
distance of 1,500 miles since none of the planes typically used by the regional
airlines can travel farther than this distance.
10. I generate lndensity to be the logged total number of passengers flying on a
route in a particular year-quarter.
11. Following Reiss and Spiller [30], I limit my data to the top 2,500 routes in order
to account for small airline markets.
12. I generate nLCC, nLEG, nREG, and nOT HER to be the number of operating
carriers on a route in a particular year-quarter that is identified as either a lowcost carriers, legacy carrier, regional airline, or other airline, respectively.
• Low-cost carriers: AirTran Airways, JetBlue Airways, Southwest Airlines,
Spirit Airlines
87
• Legacy carriers: American Airlines, Continental Airlines, Delta Air Lines,
Northwest Airlines, United Airlines, US Airways
• Legacy carrier - regional airline partnerships are outlined in Table B.1.
13. REGentry and LCCentry are generated to equal 1 in the year-quarter that a
regional airline operating carrier - legacy carrier ticketing carrier combination
or low-cost carrier ticketing carrier, respectively, starts servicing a route that
it was not on for the previous four quarters and stays on the route for at least
two consecutive quarters afterwards. The entrant must also fly at least 100
passengers in the year-quarter of entry.
• I create W N entry and otherLCCentry to equal LCCentry when the ticketing carrier is Southwest Airlines or one of the other low-cost carriers
(AirTran Airways, JetBlue Airways, and Spirit Airlines), respectively.
14. Using the Airline On-Time Performance Data aggregated to the quarterly level,
I construct pdelay, which is constructed to be the percentage of flights that
are delayed over 15 minutes on a given route in a particular year-quarter, and
merge that into the dataset.
15. Using the Local Area Personal Income tables, I construct pop, income, and
tourism and merge them into the dataset.
• pop: geometric mean of population (in millions) of endpoint airports’ MSA
in a particular year
• income: geometric mean of per capita income (in tens of thousands) of
endpoint airports’ MSA in a particular year
88
• tourism: the maximum of endpoint airports’ percentage of accommodation
income in nonfarm income in a particular year
16. I drop any routes with missing tourism or income data.
This is where the process diverts between the datasets used for the entry regressions
analyzed in Section 2.3 of Chapter 2 and the price regressions discussed in Section 2.4.
In order to get the data used for the entry regressions I continue with the following steps:
1. I collapse the variables at the route-year-quarter level.
2. I keep observations pertaining only to quarter 4 of each year in order to aggregate the data from a quarterly basis to a yearly basis.
3. I only keep routes if a legacy carrier was a ticketing carrier at any point in the
sample time period.
Summary statistics for the dataset used for entry regressions are reported in Table B.2.
In order to get the data used for the price regressions I commence from step 16
with the following steps:
1. I generate price to be the passenger-weighted average price charged by the
ticketing carrier.
• I create lnprice to be the natural log of price.
89
2. I create msapt to be simple average of the ticketing carrier’s market share at
the airport-level based on the number of passengers flown in a particular yearquarter.
3. I generate HHIapt to be the sum of squared market shares at the airport-level
in a particular year-quarter.
4. I create REGcompeting to equal 1 if a low-cost carrier competes against a
legacy carrier that switches to a regional airline and every subsequent yearquarter that the low-cost carrier competes against the regional airline / legacy
carrier partnership, and 0 otherwise.
5. I generate REGoperating to equal 1 the year-quarter that a legacy carrier starts
to outsource to a regional airline and every subsequent year-quarter that the
regional airline operates on behalf of that legacy carrier, and 0 otherwise.
I create two subsamples that isolate observations relevant to legacy carriers and lowcost carriers. In the legacy carrier subsample, I keep observations that pertain to a
legacy carrier being the ticketing carrier and a regional airline as the operating carrier.
I then keep observations if the legacy carrier decided to start using the regional airline
partner as the operating carrier on the route during the sample time period (1998:Q1
- 2009:Q4). Summary statistics for the legacy carrier sample set are reported in Table
B.3. In the low-cost carrier subsample, I keep observations where a low-cost carrier
serves as the ticketing carrier and was an incumbent when the legacy carrier switched
to a regional airline during the sample time period. Summary statistics for the legacy
carrier sample set are reported in Table B.4.
90
Table B.1: Regional Airline Partnerships
Legacy Carrier
Regional Airline Partners
American Eagle
American Airlines American Eagle/Executive
Chautauqua Airlines
Chautauqua Airlines
Cape Air
Continental Airlines Colgan Air
CommutAir
Express Jet
Gulfstream International Airlines
Atlantic Southeast Airlines
Chautauqua Airlines
Comair
Delta Air Lines
Freedom Airlines
Mesaba Airlines
Pinnacle Airlines
Shuttle America
SkyWest Airlines
Comair
Northwest Airlines Compass Airlines
Mesaba Airlines
Pinnacle Airlines
Chautauqua Airlines
Colgan Air
ExpressJet Airlines
GoJet Airlines
United Airlines
Great Lakes
Mesa Airlines
Shuttle America
SkyWest Airlines
Trans States Airlines
Air Wisconsin
Chautauqua Airlines
Colgan Air
US Airways
Mesa Airlines
Piedmont
PSA
Republic Airlines
Trans States Airlines
Source: Regional Airline Association 2009 Annual Report (www.raa.org)
91
Table B.2: Summary Statistics (Entry Sample)
Variable
Definition
REGentryit
Indicator equal to 1 if a regional airline enters on route i
in time period t, and 0 otherwise
Indicator equal to 1 if a low-cost carrier enters on route i
in time period t, and 0 otherwise
Indicator equal to 1 if Southwest Airlines enters on route i
in time period t, and 0 otherwise
Indicator equal to 1 if a low-cost carrier other than
Southwest Airlines enters on route i in time period t, and 0 otherwise
Distance (in miles) between the endpoints of route i
LCCentryit
W N entryit
otherLCCentryit
distancei
passengersit
pdelayit
popit
Number of passengers on route i in time period t
Note: lndensityit = ln(passengersit )
Percentage of flights delayed over 15 minutes on route i in time period t
HHIrouteit
Geometric mean of population (in millions) of origin and destination
airports’ MSA on route i in time period t
Geometric mean of per capita income (in tens of thousands) of origin
and destination airports’ MSA on route i in time period t
Maximum of the percentage of accommodation income in nonfarm income
of origin and destination airports’ MSA on route i in time period t
Herfindahl-Hirschman Index for route i in time period t
maxshareit
Maximum of the market share of carriers on route i in time period t
nLEGit
Number of legacy carriers operating route i in time period t
nLCCit
Number of low-cost carriers carriers operating route i in time period t
nREGit
Number of regional airlines servicing route i in time period t
nOT HERit
Number of other carriers (not legacy carrier, low-cost carrier, or regional
airline) servicing route i in time period t
Number of routes in the sample
Number of observations
incomeit
tourismit
Routes
N
92
Mean
(Std. Dev.)
0.122
(0.345)
0.028
(0.166)
0.006
(0.078)
0.023
(0.149)
723.65
(339.84)
29859.29
(29346.83)
0.202
(0.084)
3.402
(2.204)
3.582
(0.547)
1.745
(20.415)
0.578
(0.206)
0.691
(0.184)
2.647
(1.456)
0.572
(0.593)
1.057
(1.269)
0.596
(0.890)
1,161
12,790
Table B.3: Summary Statistics (Legacy Carrier Subsample)
Variable
Definition
priceijt
msrouteijt
Average one-way market fare for carrier i
on route j in time period t
Market share for carrier i on route j in time period t
HHIroutejt
Herfindahl Index for route j in time period t
msaptijt
Arithmetic mean of carrier i’s market share
at endpoints on route j in time period t
Arithmetic mean of Herfindahl Indexes
at endpoints on route j in time period t
Geometric mean of population (in millions) of origin and
destination airports’ MSA on route j in time period t
Indicator variable equal to 1 if carrier i is a legacy carrier
and switches to a regional airline on route j in time period t
or prior to time period t, and 0 otherwise
Number of routes in the sample
Number of observations
HHIaptjt
popjt
REGoperatingijt
Routes
N
Mean
(Std. Dev.)
200.50
(89.40)
0.400
(0.340)
0.598
(0.209)
0.235
(0.137)
0.333
(0.099)
3.24
(2.02)
0.313
(0.464)
1,313
91,777
Table B.4: Summary Statistics (Low-Cost Carrier Subsample)
Variable
Definition
priceijt
msrouteijt
Average one-way market fare for carrier i
on route j in time period t
Market share for carrier i on route j in time period t
HHIroutejt
Herfindahl Index for route j in time period t
msaptijt
Arithmetic mean of carrier i’s market share
at endpoints on route j in time period t
Arithmetic mean of Herfindahl Indexes
at endpoints on route j in time period t
Geometric mean of population (in millions) of origin and
destination airports’ MSA on route j in time period t
Indicator variable equal to 1 if carrier i is a low-cost carrier
and a legacy carrier switches to a regional airline on route j
in time period t or prior to time period t, and 0 otherwise
Number of routes in the sample
Number of observations
HHIaptjt
popjt
REGcompetingijt
Routes
N
93
Mean
(Std. Dev.)
134.10
(36.87)
0.344
(0.300)
0.539
(0.192)
0.267
(0.179)
0.308
(0.106)
2.82
(1.89)
0.361
(0.480)
590
25,220
Appendix C: Robustness Checks for “The Influence of
Low-Cost Carriers on the Use of Regional Airlines”
This section includes the description and results of the robustness checks discussed
in the paper.
C.1
Pooled Logit Regression Results
The regressions reported in the paper are based on a two-way fixed effects logit
model (Equations 2.1 and 2.2 in Chapter 2). I run several specifications without
the fixed effects as a robustness check. The first set of specifications provides a
robustness check for the results on the use of regional airlines as a response to current
competition, whereas the second set of specifications provides a robustness check for
the results on the use of regional airlines as a barrier to entry.
Tables C.1 and C.2 report the robustness checks for the results in Table 2.1 in
Chapter 2. Both specifications in the appendix use a pooled logit model with the key
difference being that the regression reported in Table C.2 includes time dummies to
account for year effects, whereas the regression reported in Table C.1 does not. In
both cases, the estimated coefficient for nLCC, the variable of interest, is positive
and significant as is the case in Table 2.1 in Chapter 2.
94
Tables C.3 and C.4 report the robustness checks for the results in Table 2.2 in
Chapter 2. Both specifications in the appendix use a pooled logit model with the key
difference being that the regression reported in Table C.4 includes time dummies to
account for year effects, whereas the regression reported in Table C.3 does not. In
both cases, the estimated coefficient for nREG, the variable of interest, is statistically
insignificant as is the case in Table 2.2 in Chapter 2.
C.2
Price Matching Windows
Tables 2.7 and 2.8 in Chapter 2 of the paper discuss how legacy carriers are
more likely to price match competing low-cost carriers, if they exist, once they outsource the operation of a route to a regional airline. Price matching in that table
was defined as the legacy carrier’s price being within 20% of an existing low-cost
carrier after the switch to a regional airline. Tables C.5 and C.6 report the frequency
and percentage of legacy carrier’s setting an average price that is higher, the same,
or lower than that of low-cost carriers using a 15% window in the quarter prior to
the legacy carrier outsourcing to a regional airline and in the quarter of the switch,
respectively, while Tables C.7 and C.8 similarly report the respective before and after
results utilizing a 25% window. The results suggest that legacy carriers are more
inclined to price match existing low-cost carriers once they outsource to regional airlines regardless of the window size. The caveat is that a larger price window tends to
define more instances of price matching.
95
Table C.1: Entry by Regional Airlines (Pooled Logit Model)
Dependent variable
Variable
Route distance (lndistance)
Hub airport (hub)
Multi-airport market (multiapt)
Slot-controlled airport (slots)
Market density (lndensity)
Airport congestion (pdelay)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Route concentration (HHIroute)
Maximum market share (maxshare)
Number of legacy carriers (nLEG)
Number of low-cost carriers (nLCC)
Number of regional airlines (nREG)
Number of other airlines (nOT HER)
N
REGentry
Logit
Standard
coefficient
error
-0.688**
(0.062)
0.313**
(0.064)
-0.404**
(0.063)
0.551**
(0.075)
-0.077*
(0.031)
1.116**
(0.253)
-0.013
(0.015)
0.056
(0.048)
-0.045**
(0.009)
-1.190*
(0.547)
-0.388
(0.581)
0.026
(0.024)
0.113**
(0.019)
-0.367**
(0.057)
-0.068*
(0.029)
18,889
Note: This table presents the results for the logit cross-section regression model on entry by regional airlines. Entry
is defined when a legacy carrier switches operation of a route to a regional airline. Standard errors are clustered by
route to account for correlation within a route over time. Observations are at the route-year level.
* indicates significance at 5% level.
** indicates significance at 1% level.
96
Table C.2: Entry by Regional Airlines (Pooled Logit Model with Time Dummies)
Dependent variable
Variable
Route distance (lndistance)
Hub airport (hub)
Multi-airport market (multiapt)
Slot-controlled airport (slots)
Market density (lndensity) lndensity
Airport congestion (pdelay)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Route concentration (HHIroute)
Maximum market share (maxshare)
Number of legacy carriers (nLEG)
Number of low-cost carriers (nLCC)
Number of regional airlines (nREG)
Number of other airlines (nOT HER)
N
REGentry
Logit
Standard
coefficient
error
-0.773**
(0.065)
0.315**
(0.068)
-0.415**
(0.066)
0.599**
(0.080)
-0.021
(0.033)
1.637**
(0.284)
-0.021
(0.016)
-0.121
(0.089)
-0.048**
(0.009)
-1.273*
(0.572)
-0.319
(0.611)
0.047
(0.025)
0.050*
(0.022)
-0.449**
(0.062)
-0.058
(0.030)
18,889
Note: This table presents the results for the logit cross-section regression model on entry by regional airlines. Entry
is defined when a legacy carrier switches operation of a route to a regional airline. Observations are at the
route-year level. Time dummies suppressed. Standard errors are clustered by route to account for correlation within
a route over time.
* indicates significance at 5% level.
** indicates significance at 1% level.
97
Table C.3: Entry by Low-Cost Carriers Airlines (Pooled Logit Model)
Dependent variable
Variable
Route distance (lndistance)
Hub airport (hub)
Multi-airport market (multiapt)
Slot-controlled airport (slots)
Market density (lndensity)
Airport congestion (pdelay)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Route concentration (HHIroute)
Maximum market share (maxshare)
Number of legacy carriers (nLEG)
Number of low-cost carriers (nLCC)
Number of regional airlines (nREG)
Number of other airlines (nOT HER)
N
LCCentry
Logit
Standard
coefficient
error
0.367*
(0.147)
-0.774**
(0.168)
-0.912**
(0.140)
-0.756**
(0.222)
0.292**
(0.095)
-0.310
(0.661)
0.113**
(0.033)
-0.272*
(0.112)
-0.024
(0.015)
-0.789
(1.291)
0.587
(1.405)
0.111*
(0.048)
-1.172**
(0.177)
-0.025
(0.059)
-0.339
(0.079)
12,789
Note: This table presents the results for the logit cross-section regression model on entry by four low-cost carriers
(AirTran Airways, JetBlue Airways, Southwest Airlines, and Spirit Airlines). Standard errors are clustered by route
to account for correlation within a route over time. Observations are at the route-year level.
* indicates significance at 5% level.
** indicates significance at 1% level.
98
Table C.4: Entry by Low-Cost Carriers (Pooled Logit Model with Time Dummies)
Dependent variable
Variable
Route distance (lndistance)
Hub airport (hub)
Multi-airport market (multiapt)
Slot-controlled airport (slots)
Market density (lndensity)
Airport congestion (pdelay)
Population (pop)
Per capita income (income)
Tourist market (tourism)
Route concentration (HHIroute)
Maximum market share (maxshare)
Number of legacy carriers (nLEG)
Number of low-cost carriers (nLCC)
Number of regional airlines (nREG)
Number of other airlines (nOT HER)
N
LCCentry
Logit
Standard
coefficient
error
0.398**
(0.149)
-0.704**
(0.172)
-0.978**
(0.143)
-0.657**
(0.227)
0.213*
(0.103)
-1.679*
(0.796)
0.112**
(0.032)
0.167
(0.203)
-0.017
(0.015)
-0.127
(1.309)
-0.145
(1.411)
0.100*
(0.049)
-1.082**
(0.187)
-0.038
(0.067)
-0.322**
(0.082)
12,789
Note: This table presents the results for the logit cross-section regression model on entry by four low-cost carriers
(AirTran Airways, JetBlue Airways, Southwest Airlines, and Spirit Airlines). Observations are at the route-year
level. Time dummies suppressed. Standard errors are clustered by route to account for correlation within a route
over time.
* indicates significance at 5% level.
** indicates significance at 1% level.
99
Legacy Carrier
Legacy Carrier
Legacy Carrier
Table C.5: Frequency of Price Matching Before Outsourcing: 15% Window
Legacy Carrier’s Price > Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
29 (54.7%) 2 (100.0%) 30 (37.5%) 2 (100.0%)
Continental 13 (81.3%)
–
9 (30.0%)
–
Delta
171 (62.6%) 13 (52.0%) 62 (33.7%)
0 (0.0%)
Northwest
76 (66.7%)
–
46 (56.1%) 8 (100.0%)
United
35 (74.5%) 10 (100.0%) 32 (51.6%) 2 (100.0%)
US Airways 74 (65.5%)
6 (50.0%)
49 (73.1%) 14 (100.0%)
Legacy Carrier’s Price = Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
16 (30.2%)
0 (0.0%)
41 (51.3%)
0 (0.0%)
Continental
0 (0.0%)
–
21 (70.0%)
–
Delta
82 (30.0%) 12 (48.0%) 107 (58.2%)
0 (0.0%)
Northwest
25 (21.9%)
–
28 (34.1%)
0 (0.0%)
United
11 (23.4%)
0 (0.0%)
21 (33.9%)
0 (0.0%)
US Airways 37 (32.7%)
4 (33.3%)
13 (19.4%)
0 (0.0%)
Legacy Carrier’s Price < Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
8 (15.1%)
0 (0.0%)
9 (11.3%)
0 (0.0%)
Continental
3 (18.8%)
–
0 (0.0%)
–
Delta
20 (7.3%)
0 (0.0%)
15 (8.2%)
1 (100.0%)
Northwest
13 (11.4%)
–
8 (9.8%)
0 (0.0%)
United
1 (2.1%)
0 (0.0%)
9 (14.5%)
0 (0.0%)
US Airways
2 (1.8%)
2 (16.7%)
5 (7.5%)
0 (0.0%)
Note: This table reports the frequency and percentages of the occurrence that a legacy carrier sets an average price
that is higher than, similar to, or lower than that of incumbent low-cost carriers in the quarter prior to when a
legacy carrier switches the operation of a route to a regional airline. A dash signifies that the legacy carrier never
switched to a regional airline when the low-cost carrier exists on the route. A window of 15% defines price matching
when the legacy carrier’s average price is within 15% of the price charged by the low-cost carrier.
100
Legacy Carrier
Legacy Carrier
Legacy Carrier
Table C.6: Frequency of Price Matching After Outsourcing: 15% Window
Legacy Carrier’s Price > Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
16 (30.2%)
0 (0.0%)
26 (32.5%) 2 (100.0%)
Continental 11 (68.8%)
–
9 (30.0%)
–
Delta
160 (58.6%) 2 (8.0%)
29 (15.8%)
0 (0.0%)
Northwest
72 (63.2%)
–
38 (46.3%) 8 (100.0%)
United
28 (59.6%) 9 (90.0%) 31 (50.0%) 2 (100.0%)
US Airways 76 (67.3%) 9 (75.0%) 43 (64.2%) 13 (92.9%)
Legacy Carrier’s Price = Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
31 (58.5%) 1 (50.0%) 44 (55.0%)
0 (0.0%)
Continental
1 (6.3%)
–
15 (50.0%)
–
Delta
91 (33.3%) 18 (72.0%) 125 (67.9%) 0 (0.0%)
Northwest
32 (28.1%)
–
33 (40.2%)
0 (0.0%)
United
15 (31.9%)
0 (0.0%)
21 (33.9%)
0 (0.0%)
US Airways 30 (26.5%) 3 (25.0%) 24 (35.8%)
1 (7.1%)
Legacy Carrier’s Price < Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
6 (11.3%)
1 (50.0%) 10 (12.5%)
0 (0.0%)
Continental
4 (25.0%)
–
6 (20.0%)
–
Delta
22 (8.1%)
5 (20.0%) 30 (16.3%) 1 (100.0%)
Northwest
10 (8.8%)
–
11 (13.4%)
0 (0.0%)
United
4 (8.5%)
1 (10.0%) 10 (16.1%)
0 (0.0%)
US Airways
7 (6.2%)
0 (0.0%)
0 (0.0%)
0 (0.0%)
Note: This table reports the frequency and percentages of the occurrence that a legacy carrier sets an average price
that is higher than, similar to, or lower than that of incumbent low-cost carriers in the quarter when a legacy carrier
switches the operation of a route to a regional airline. A dash signifies that the legacy carrier never switched to a
regional airline when the low-cost carrier exists on the route. A window of 15% defines price matching when the
legacy carrier’s average price is within 15% of the price charged by the low-cost carrier.
101
Legacy Carrier
Legacy Carrier
Legacy Carrier
Table C.7: Frequency of Price Matching Before Outsourcing: 25% Window
Legacy Carrier’s Price > Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
25 (47.2%) 2 (100.0%) 22 (27.5%) 2 (100.0%)
Continental 11 (68.8%)
–
7 (23.3%)
–
Delta
129 (47.3%) 13 (52.0%) 48 (26.1%)
0 (0.0%)
Northwest
65 (57.0%)
–
34 (41.5%) 7 (87.5%)
United
32 (68.1%) 6 (60.0%) 25 (40.3%) 2 (100.0%)
US Airways 62 (54.9%) 4 (33.3%) 44 (65.7%) 13 (92.9%)
Legacy Carrier’s Price = Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
24 (45.3%)
0 (0.0%)
51 (63.8%)
0 (0.0%)
Continental
4 (25.0%)
–
23 (76.7%)
–
Delta
128 (46.9%) 12 (48.0%) 133 (72.3%) 0 (0.0%)
Northwest
39 (34.2%)
–
46 (56.1%) 1 (12.5%)
United
15 (31.9%) 4 (40.0%) 28 (45.2%)
0 (0.0%)
US Airways 49 (43.4%) 6 (50.0%) 19 (28.4%)
1 (7.1%)
Legacy Carrier’s Price < Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
4 (7.5%)
0 (0.0%)
7 (8.8%)
0 (0.0%)
Continental
1 (6.3%)
–
0 (0.0%)
–
Delta
16 (5.9%)
0 (0.0%)
3 (1.6%)
1 (100.0%)
Northwest
10 (8.8%)
–
2 (2.4%)
0 (0.0%)
United
0 (0.0%)
0 (0.0%)
9 (14.5%)
0 (0.0%)
US Airways
2 (1.8%)
2 (16.7%)
4 (6.0%)
0 (0.0%)
Note: This table reports the frequency and percentages of the occurrence that a legacy carrier sets an average price
that is higher than, similar to, or lower than that of incumbent low-cost carriers in the quarter prior to when a
legacy carrier switches the operation of a route to a regional airline. A dash signifies that the legacy carrier never
switched to a regional airline when the low-cost carrier exists on the route. A window of 25% defines price matching
when the legacy carrier’s average price is within 25% of the price charged by the low-cost carrier.
102
Legacy Carrier
Legacy Carrier
Legacy Carrier
Table C.8: Frequency of Price Matching After Outsourcing: 25% Window
Legacy Carrier’s Price > Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
11 (20.8%)
0 (0.0%)
13 (16.3%) 2 (100.0%)
Continental
7 (43.8%)
–
7 (23.3%)
–
Delta
120 (44.0%) 1 (4.0%)
15 (8.2%)
0 (0.0%)
Northwest
60 (52.6%)
–
26 (31.7%) 5 (62.5%)
United
23 (48.9%) 6 (60.0%) 22 (35.5%) 2 (100.0%)
US Airways 65 (57.5%) 5 (41.7%) 31 (46.3%) 13 (92.9%)
Legacy Carrier’s Price = Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
41 (77.4%)
2 (0.0%)
60 (75.0%)
0 (0.0%)
Continental
6 (37.5%)
–
22 (73.3%)
–
Delta
149 (54.6%) 23 (92.0%) 158 (85.9%) 0 (0.0%)
Northwest
50 (43.9%)
–
50 (61.0%) 3 (37.5%)
United
22 (26.8%) 4 (40.0%) 35 (56.5%)
0 (0.0%)
US Airways 47 (41.6%) 7 (58.3%) 36 (53.7%)
1 (7.1%)
Legacy Carrier’s Price < Low-Cost Carrier’s Price
Low-Cost Carrier
AirTran
JetBlue
Southwest
Spirit
American
1 (1.9%)
0 (0.0%)
7 (8.8%)
0 (0.0%)
Continental
3 (18.8%)
–
1 (3.3%)
–
Delta
4 (1.5%)
1 (4.0%)
11 (6.0%) 1 (100.0%)
Northwest
4 (3.5%)
–
6 (7.3%)
0 (0.0%)
United
2 (4.3%)
0 (0.0%)
5 (8.1%)
0 (0.0%)
US Airways
1 (0.9%)
0 (0.0%)
0 (0.0%)
0 (0.0%)
Note: This table reports the frequency and percentages of the occurrence that a legacy carrier sets an average price
that is higher than, similar to, or lower than that of incumbent low-cost carriers in the quarter when a legacy carrier
switches the operation of a route to a regional airline. A dash signifies that the legacy carrier never switched to a
regional airline when the low-cost carrier exists on the route. A window of 25% defines price matching when the
legacy carrier’s average price is within 25% of the price charged by the low-cost carrier.
103
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